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{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: hello, what does 'weighting the activations of the final convolutional layer by their corresponding gradients and averaging the resulting weights spatially' mean? Explainer: The classification model will extract some features from input images. Explainer: This is the the activations of the final convolutional layer. Explainer: Then Grad-cam calculates how changes to each feature would affect the classification model's output. Explainer: If the model's prediction is sensitive to some features, it means those features are of high importance. Seeker: how does the model know what features to extract? Explainer: The classification model will automatically learn from the data. Seeker: what makes the model sensitive to certain features? Explainer: If the model relies more on certain features, little change in those features will affect the model's prediction. Seeker: like the change in background? Explainer: For example, in the second image on the left side, regions in red color means the features that are important to the model's prediction. Explainer: like the body of the fish Seeker: okay understand Seeker: I have no more questions"
} | {
"output": "Explainer: changing those regions will affect more to the model's prediction comparing to other regions in the blue color. Thanks for your questions"
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: What about ai using image based recognition by utilising through experience"
} | {
"output": "Explainer: Sorry, I don't understand your question."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: what is the image classification model Explainer: It is a deep learning model that can classify an image into a category Explainer: For the example on the left screen, the classification model classifies the first image as goldfish. Seeker: can you tell me a little more about the swin transformer? Explainer: Sure. The Swin Transformer is a type of deep learning model designed to analyze images, which works by dividing the image into small parts and paying selective attention to each part, thereby allowing it to handle high-quality, detailed images more efficiently. Explainer: swin transformer performs well on image-related tasks, like the image classification here. Seeker: is it right to say that the Grad-CAM method is more of a visualization method for the data processing by the Swin Transformer? Explainer: Yes. The grad-CAM method visualized how the Swin Transformer makes a prediction of an image. Seeker: can you explain the following sentence in simpler terms? \"This heatmap is generated by weighting the activations of the final convolutional layer by their corresponding gradients and averaging the resulting weights spatially.\" Explainer: The swin transformer has many neural layers, which can extract features of images. the convolutional layer is one of these neural layers. The activations of the convolutional layer contains the features for each image regions. Explainer: Grad-cam generate the second heatmap image by calculating how changes to feature of each image region would affect the model's output. Explainer: When we change a region of the image and the model's output changes a lot, this means that image region is important to the model prediction. Explainer: Bluer colors are used to represent areas of low importance, while redder colors indicate areas of high importance. Seeker: does that mean that brighter colors in the actual image equate to changes in the image region? Explainer: No. When changing images, we will equally change feature of image regions. Seeker: so how do you weight the activations of the convolutional layer? Explainer: activations of the convolutional layer contain extracted features of images. For each region of images, there are multiple features. Explainer: We will calculate how changes in each feature for each image region will affect the models prediction. Seeker: what are some features? Explainer: That's automatically learned by the classification model. Seeker: would you classify the model as a black box?"
} | {
"output": "Explainer: The classification model uses these features to understand the image. Yes. The classification model is black box model."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: does the Grad-CAM retroactively explain the explanation based on deep learning output? Explainer: Sorry, what do you mean by retroactively? Explainer: Grad-cam will generate explanations after a learning model makes a prediction. Seeker: oh I mean the explanation is generated after the fact (i.e., the classification is already done, and Grad-CAM tries to find out why the model carries out its classification in a certain way) Seeker: Yep I think that's retroactive! Explainer: Yes. that's how Grad-cam works. Seeker: thanks! Seeker: what does this sentence mean: This heatmap is generated by weighting the activations of the final convolutional layer by their corresponding gradients and averaging the resulting weights spatially. Seeker: particularly, the term \"final convolutional layer\" Explainer: The classification model will contain many layers to extract features from the image. the convolutional layer is one of them. Seeker: what other layers are there? Explainer: Grad-CAM works by analyzing the output of the final convolutional layer of the network and calculating how changes to each region of the image would affect the network's output. Seeker: how effective is grad-cam in explaining the prediction as compared to other methods?"
} | {
"output": "Explainer: Other layers are transformer layers and full-connected layers. Grad-cam explains deep learning models directly from the feature they extracted, and it performs well when explaining image-related models."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: what is grad-cam? Explainer: Grad-cam is the name of the explanation method. Explainer: It is used to explain the prediction from the classification model Explainer: There is a classification model that can classify an image into a category. Here it classifies the image as a goldfish. Explainer: But the classification model is complex. We don't understand it. We don't know how it predicts the image as a goldfish. Explainer: Then we use the grad-cam, a explanation model to generate the second image, which tries to explain which region the classification model focuses on to make such decision. Seeker: Interesting Seeker: In the event that there is more than 1 important region in an image (eg the image has a goldfish and a shark), will the grad-cam be able to detect both creatures?"
} | {
"output": "Explainer: Grad-cam actually cannot detect features. It is used to explain the prediction of the classification model, which is swin transformer here. But if you mean whether the swin transformer can detect both creatures, it may do it."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: does it identify the specific type of animal instead of the general identification? Explainer: This will depend on the dataset used to train the classification model. The model here can identify specific type of animal. Explainer: Like different species of fish, cats, or dogs. Seeker: ohh thats kinda amazing Seeker: is it possible for it to detect multiple animals at once? Seeker: what is the accuracy level? Explainer: For the model on the left side, it can only detect one animal at once. But as long as we have data, we can train the model to detect multiple animals at once. Explainer: The accuracy is 84% on very large dataset (imagenet) Seeker: ohh Explainer: Even though the classification model performs pretty well, how the classification model predicts a category for an image is not understandable to humans. So we have the Grad-cam to explain the behavior of the classification model. Seeker: oh, understand Explainer: Grad-cam generates a heatmap that highlights the regions of the image that are most important for the prediction, which is the second image on the left screen. Explainer: Do you have questions about the Grad-cam method? Seeker: can this method be used on humans next time for identification or tracking etc.? Seeker: or specifically for animals only? Explainer: This method is applied to certain types of deep learning models. Explainer: Do you mean use Grad-cam to explain deep learning models for detecting humans? Seeker: identifying as in the identify of the person for tracking"
} | {
"output": "Explainer: Sorry, I didn't understand you questions. Can you rephrase your last question?"
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: Why is the model prediction only part of the leopard's face? Explainer: The classification model's prediction is the image contains a leopard. Seeker: Why are only certain parts of the picture highlighted?"
} | {
"output": "Explainer: The second image is the explanation of this model prediction. It tries to explain which region the classification model focuses on to make such decision"
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: does the Grad-CAM retroactively explain the explanation based on deep learning output? Explainer: Sorry, what do you mean by retroactively? Explainer: Grad-cam will generate explanations after a learning model makes a prediction. Seeker: oh I mean the explanation is generated after the fact (i.e., the classification is already done, and Grad-CAM tries to find out why the model carries out its classification in a certain way) Seeker: Yep I think that's retroactive! Explainer: Yes. that's how Grad-cam works. Seeker: thanks! Seeker: what does this sentence mean: This heatmap is generated by weighting the activations of the final convolutional layer by their corresponding gradients and averaging the resulting weights spatially. Seeker: particularly, the term \"final convolutional layer\" Explainer: The classification model will contain many layers to extract features from the image. the convolutional layer is one of them. Seeker: what other layers are there? Explainer: Grad-CAM works by analyzing the output of the final convolutional layer of the network and calculating how changes to each region of the image would affect the network's output. Seeker: how effective is grad-cam in explaining the prediction as compared to other methods? Explainer: Other layers are transformer layers and full-connected layers. Explainer: Grad-cam explains deep learning models directly from the feature they extracted, and it performs well when explaining image-related models. Seeker: are there comparative studies comparing grad-cam with other methodologies? Seeker: what are some applications of grad-cam? Can we use grad-cam explanations to improve deep learning models? Explainer: Yes. There are many other explanation methods, like LIME, or integrated gradients, and also many studies to compare them. Seeker: How do LIME or integrated gradients compare with grad-CAM in terms of explanation robustness? Explainer: Yes, we can. Using image classification as an example, if a model consistently assigns higher attribution scores to the background regions when classifying an image into the leopard category, this could indicate the model focused on the wrong features to decide the leopard category. Explainer: Grad-cam is more robust compared to the other two explanation methods. Explainer: But the other two methods are model agnostic, which means those methods can be used to explain any type of deep learning model. Seeker: whereas grad-cam is exclusively for visual deep learning? Explainer: yes. grad-cam is exclusively for a certain type of deep learning models, which is usually used in visual tasks. Seeker: i see. thank you! Seeker: It would be nice to know if the details of the gradient calculation although I think it will be too math intensive for me to understand"
} | {
"output": "Explainer: Sure. I can explain it to you. the final convolutional layer of the classification model will generate feature maps of the original image. Grad-CAM will calculate the gradients of the output class with respect to each features in the feature maps The gradients are how changes to each feature would affect the network's output."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: So to summarise up about the description, the grad-CAM which generates a heatmap is able to identify the image/object in the picture using deep learning model? Explainer: Not exactly. There is a deep learning model that is used to classify an image into a category. Explainer: This is the classification model. Explainer: The first image is the input of the classification model. The classification mode classifies it as a goldfish. Explainer: But the classification model is complex. We don't understand it. We don't know how it predicts the image as a goldfish. Explainer: So we use grad-cam to explain which region the classification model focuses on to make such decision Seeker: ohhhh i get it now Seeker: but the grad-CAM which generates a heatmap shows high importance (indicated in red), could be anything. how would the grad-cam be able to detect the classification model Seeker: would the grad-CAM in any sense, detect wrongly and give a wrong classification/answer? Explainer: The heatmap generated by Grad-cam is based on the classification model. Explainer: The classification model will extract some features from original images. Explainer: Then the grad-cam generates the heatmap by calculating how changes in these features will affect the classification model's prediction. Explainer: it could be possible. Seeker: so the grad-cam is not a 100% method proof right Explainer: Yes. Seeker: in that case, how would the grad-cam help to assist in like progressing of any research Explainer: Enough though it is not 100% correct, the heatmap generated by Grad-cam highly depends on the classification model. Explainer: It can help users understand why a model made a specific prediction and may increase users' trust in the classification models. Explainer: For research, it can help them debugs the model. Explainer: Like, if the classification model made a wrong decision, we may use grad-cam to observe why it made such a wrong decision, is it because it focuses on the wrong areas of images. Seeker: okay okay Explainer: The researcher can those explanations from grad-cam to update the model. Seeker: so all in all, it is also able to help be a predictive and preventive kind of analysis Explainer: Yes. Explainer: It helps us better understand the \"black box\" deep learning model. Seeker: okay Explainer: Do you have other questions? Seeker: not at the moment"
} | {
"output": "Explainer: Thanks for your questions."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: What animals are more easy to read?"
} | {
"output": "Explainer: Animals that have more features that others don't have. But it also depends on the classification model and the dataset used to train the classification model."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: what is swim transformer Explainer: The Swin Transformer is a type of artificial intelligence model designed to analyze images, which works by dividing the image into small parts and paying selective attention to each part, thereby allowing it to handle high-quality, detailed images more efficiently. Explainer: Here we use the swin transformer to classify an image into a category. Seeker: what is LIME Explainer: LIME is a method to explain a specific prediction of a deep learning model by highlighting the most important features among input features. Explainer: Here we use LIME to explain a prediction from swin transformer. Seeker: whats the yellow spots for Explainer: These are regions of an image that are most important for the classification model's prediction. Seeker: ok! Explainer: Here the regions with yellow lines explain how the swin transformer predicts the first image into the leopard category. Seeker: ok Seeker: can u detect more complex things other than animals Seeker: or more specific \nlike what breed Explainer: It will depend on the dataset we used to train the classification model. The classification model will automatically learn how to identify objects from the data. Explainer: The swin transformer here can identify different breeds of birds, fish, or other animals. Seeker: the first part of the questions just now was very confusing the one where you put 3 similar pictures Seeker: then ask to choose one Seeker: there was no specific question and it was rather confusing"
} | {
"output": "Explainer: In these three options, we have three classification models with different accuracy. We use an explanation model to explain how they make a prediction. A good classfication model should focus on the area containing visual characters that are unique to the category, e.g., Ears of cats, and strips of zebra. These are the important regions. A good model also should focus on more unique visual characters as possible. And we want you to choose among three options which one is the best classification model based on the explanation of their prediction."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: How does the method arrive at the prediction based solely on the generated heatmap?"
} | {
"output": "Explainer: The classification model will use the entire image to make a prediction. But it is very complex and we don't understand how it makes such a prediction. We use grad-cam to explanation the model's prediction by highlighing regions of an image that are most important for a deep neural network's prediction. So heatmap is used to explain how a classification model makes a prediction after the classification model has made an prediction."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: what is the factor in the explanation for the model prediction that indicated that it was a leopard Explainer: Here as we can observe from the second image, the explanation model highlight some regions with yellow line to explain to us how the classification model get its prediction. Seeker: Ok... Explainer: The classification model is very complex. We cannot directly understand how it predicts the image as a leopard. Explainer: So we use an explanation model, LIME, to try to explain how the classification model gets the prediction. Explainer: And the explanation is the second image. Seeker: Ok, so in summary the LIME framework helps to identify what the image is?"
} | {
"output": "Explainer: In summary, the LIME method explain the prediction from a deep learning classification model The deep learning classification model predicts or identifies what the image is."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: Why is the model prediction only part of the leopard's face? Explainer: The classification model's prediction is the image contains a leopard. Seeker: Why are only certain parts of the picture highlighted? Explainer: The second image is the explanation of this model prediction. It tries to explain which region the classification model focuses on to make such decision Seeker: Why is this model better than other state of the art models? Explainer: It means the highlighted parts are the most important regions thatmake the classification model classifies this image into the leopard. Explainer: Do you mean the explanation model or the classification model? Seeker: Ah okay I understand now. The LIME model is the explanation model Explainer: yes. Seeker: How does it work then? How will it choose which data points to highlight? Explainer: LIME works by training a local surrogate model around a specific prediction. Explainer: The surrogate model is a white box model and inherently interpretable. Explainer: An example surrogate model is the linear model. Explainer: The output is the sum of input features with weights. The weights be used to indicate the importance of input features. Seeker: so the surrogate model will be trained on the prediction outputs of the transformer used to predict the image? Seeker: ah okay understand Explainer: yes. the surrogate model will be trained on the prediction outputs of the transformer Seeker: ok understood, thanks! Explainer: Do you have other questions? Seeker: nope"
} | {
"output": "Explainer: Thanks for your questions."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen Seeker: How does the simpler model work? Explainer: For an input data point, we can perturb around this data point and get predictions from complex classification models. Then we use these data samples to train the simple surrogate model. Therefore the simpler model can mimic the behavior of the complex model around one data point Explainer: As we understand the behavior of the simpler model, we can know the importance of each image region when the simpler model does a prediction. Seeker: What metrics do you use to measure in the model? Colours? Pixels? Explainer: Do you mean how we measure the classification model? Seeker: Yes Explainer: We measure the classification model by the accuracy of predicting correct labels. Seeker: How many images do you need in order to train a data set? Explainer: It will depend on the model size and the complexity of the task. For the image classification model here, it is trained with 1.2 million labeled images. Seeker: Is this image classification tool going to be used by someone well verse with AI and it's terms? Seeker: Because I feel that the explanation can be quite complex for someone that is not well versed in it"
} | {
"output": "Explainer: For the classification model, anyone can use it. We input an image to the model, the model will output it's prediction about the image. For the explanation method, it is quite complex for someone that is not well versed in it Here, you can ask any questions about the explanation method."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: Why uuse blue and red? Explainer: Because redder colors are usually associated with warmth and high energy while bluer colors are associated with coolness and low energy. Explainer: That is decided by the authors of the method. It's also okay to use other color pairs in the heatmap. Seeker: Why present it using a heatmap ? Explainer: This visual representation allows both technical and non-technical individuals to understand what parts of an image are contributing most to a model's prediction Explainer: A heatmap is like a colorful overlay on the picture that helps you see what the classification model is focusing on. Seeker: Tell me more about the GRAD-cam method. Explainer: sure Explainer: Grad-CAM is a technique to generate explanations by highlighting regions of an image that are most important for a classification model's prediction. Seeker: Can this method be applied to non living things as well?"
} | {
"output": "Explainer: It works by calculating the derivative of the output class with respect to the feature maps in the final convolutional layer of the classification model and using these derivatives to weight the feature maps. It depends on the dataset that is used to train the classification model. Here the classification model can also predict non-living objects in the image."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: What animals are more easy to read? Explainer: Animals that have more features that others don't have. Explainer: But it also depends on the classification model and the dataset used to train the classification model. Seeker: Ok thanks Explainer: Can you ask more questions, especially about the explanation method? Seeker: How accurate is heatmap that highlights the important regions of the image for the prediction. Seeker: And what’s the % accuracy of Grad-CAM method Explainer: We cannot measure the accuracy of the Grad-CAM method as we don't have the ground truth of how the classification model makes a prediction. Explainer: But in general, it works well. Seeker: How do u weighting the activations of the final convolutional layer by their corresponding gradients Explainer: It will calculate how changes to each region of the image would affect the classification model's output. Explainer: If the model's output is sensitive to some areas of the input images, it means those areas are important for the model's prediction. Seeker: Ok thanks Seeker: What other explanations method are there other than grad-cam"
} | {
"output": "Explainer: There are some other explanation methods, like, LIME, integrated gradients and SHAP."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: So to summarise up about the description, the grad-CAM which generates a heatmap is able to identify the image/object in the picture using deep learning model?"
} | {
"output": "Explainer: Not exactly. There is a deep learning model that is used to classify an image into a category. This is the classification model. The first image is the input of the classification model. The classification mode classifies it as a goldfish. But the classification model is complex. We don't understand it. We don't know how it predicts the image as a goldfish. So we use grad-cam to explain which region the classification model focuses on to make such decision"
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: what is swin transformer Seeker: explain lime Explainer: It is the classification model we used to classify an image into a category. It performs well on image-related tasks. Explainer: For the example on the left screen, the classification model, swin transformer, classifies the first image as a leopard. Seeker: why is the explanation inaccurate Explainer: LIME is a method to explain a specific prediction of a deep learning model by highlighting the most important features among input features. Seeker: what happens when there are more than 1 animal Explainer: Using the example on the left screen, the input is an image and the output is class Leopard. The highlighted part of the original image is the reason why the classification model predicts the input image into a class leopard. Explainer: Sorry, Can you specify this question? Seeker: the explanation does not highlight all the parts of the leopard, why Explainer: The classification model will have a high probability to classify the image into all animals. Explainer: When we observe how the image is classified into one animal, a good model should focus on the area of that animal. Explainer: On the other hand, when we observe how the image is classified into another animal, a good classification model should focus on the area of another animal. Seeker: can lime be used without internet Explainer: The classification model will use the entire image to make a prediction. But it is very complex and we don't understand how it makes such a prediction. So we use LIME to explain which regions are most important for the classification model to make a prediction. The LIME method outputs the top-5 regions that are most important. Explainer: Yes. It can. Explainer: That's why the explanation didn't highlight all the parts of the leopard Seeker: how does lime compare with other similiar software"
} | {
"output": "Explainer: The LIME method can be applied to any type of deep learning model. Some other methods are model-specific, which means they can only be applied to a certain type of deep learning model. But LIME also has some drawbacks, e.g., it may be unstable. Also, the type of surrogate models used in the LIME method also will affect the quality of explanations."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: how did u decide on which part of the image has the heatmap Explainer: The explanation method, Grad-CAM, generate the heatmap by calculating how changes to each region of the image would affect the classification model's output Seeker: what is the probability for an error of prediction Explainer: If changes of an image region will highly affect the model's prediction, that image region will be redder. Explainer: For the classification model, the accuracy is 84%. So the probability of error is 16%. Seeker: what if there are overlapping features of other objects, will it be able to tell both objects Explainer: It depends. If objects' unique visual characters are not hidden by others, the classification model may identify them. Explainer: But the classification model's results will depend on the data it sees in the training data and also depends on what it learns from the data. Seeker: ok Seeker: am I supposed to keep on asking questions ? Explainer: Yes. Can you ask more questions? Seeker: will the background of the image affect the recognition of the object if the background contains similar unique features as the image Explainer: Yes. It's possible. As the classification model will automatically learn unique features for a category from data, we don't know what parts of the image the classification model will focus on to make a prediction. Explainer: Sometimes it may focus on the wrong area to make a prediction. Explainer: No matter the prediction is correct or not. Seeker: ok Explainer: With the explanation, grad-cam, we can observe which regions the classification model focuses on to make a prediction. We can debug the model based on the explanation. Seeker: there is also a possibility that insufficient data is fed to the training data leading to a false prediction ? Seeker: as some objects might be unique in different ways Explainer: Yes, exactly. insufficient data can lead to false predictions. Seeker: am I asking the questions in the right direction Seeker: I feel like I just throwing random questions Explainer: For example, if there is not enough data, a classification model may use the appearance of water to predict the goldfish. Explainer: Very good questions. Explainer: You can ask any questions. You can also ask questions about the explanation method. Seeker: what kind of context are you planning to use these model for eg learning? school? identify features in an environment? etc"
} | {
"output": "Explainer: The classification models are fundamental models for many other tasks, like face recognition or self-driving. For the explanation method, it can be used in many sensitive areas, like healthcare, finance, and legal systems We can use the explanation method to understand the reasons behind a machine learning model's prediction."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: what is the factor in the explanation for the model prediction that indicated that it was a leopard Explainer: Here as we can observe from the second image, the explanation model highlight some regions with yellow line to explain to us how the classification model get its prediction. Seeker: Ok... Explainer: The classification model is very complex. We cannot directly understand how it predicts the image as a leopard. Explainer: So we use an explanation model, LIME, to try to explain how the classification model gets the prediction. Explainer: And the explanation is the second image. Seeker: Ok, so in summary the LIME framework helps to identify what the image is? Explainer: In summary, the LIME method explain the prediction from a deep learning classification model Explainer: The deep learning classification model predicts or identifies what the image is. Seeker: Would it be possible to identify different people, using the deep learning classification model? Seeker: If the AI were fed the data Explainer: Yes. We can do it with corresponding data. Seeker: What is swim transformer then? Explainer: Swin transformer is the classification model, which an classify an image into a category. Explainer: Swin transformer is a very complex model and contains many neural layers, e.g., convolutional layers, and transformer layers. Explainer: it can classify an image into a category. But we cannot know which regions of the image it focuses on to do a prediction. Seeker: how advanced is the swim transformer -- if the predefined categories are races and the visual input was a person of mixed decent would it be able to categorise Seeker: sure, thanks for explaining Explainer: The swin transformer is pretty accurate in a very large dataset. It can reach 84% on that dataset. Explainer: But I'm not sure whether it can perform well in the case you mentioned. Seeker: yup Explainer: Do you have other questions? Seeker: I think I understand the gist of it Seeker: none, thank you for explaining!"
} | {
"output": "Explainer: Thanks for your questions."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: how did u decide on which part of the image has the heatmap Explainer: The explanation method, Grad-CAM, generate the heatmap by calculating how changes to each region of the image would affect the classification model's output Seeker: what is the probability for an error of prediction Explainer: If changes of an image region will highly affect the model's prediction, that image region will be redder. Explainer: For the classification model, the accuracy is 84%. So the probability of error is 16%. Seeker: what if there are overlapping features of other objects, will it be able to tell both objects Explainer: It depends. If objects' unique visual characters are not hidden by others, the classification model may identify them. Explainer: But the classification model's results will depend on the data it sees in the training data and also depends on what it learns from the data. Seeker: ok Seeker: am I supposed to keep on asking questions ? Explainer: Yes. Can you ask more questions? Seeker: will the background of the image affect the recognition of the object if the background contains similar unique features as the image Explainer: Yes. It's possible. As the classification model will automatically learn unique features for a category from data, we don't know what parts of the image the classification model will focus on to make a prediction. Explainer: Sometimes it may focus on the wrong area to make a prediction. Explainer: No matter the prediction is correct or not. Seeker: ok"
} | {
"output": "Explainer: With the explanation, grad-cam, we can observe which regions the classification model focuses on to make a prediction. We can debug the model based on the explanation."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: Can you define \"swim transformer\" Explainer: It is the classification model we used to classify an image into a category. It performs well on image-related tasks. Explainer: For the example on the left screen, the classification model, swin transformer, classifies the first image as a leopard. Seeker: I see, what abou the picture depicting the explanation for the model prediction. Are the areas which are not voided help the model identify why the image is a leopard? Explainer: The classification model will use the entire image to make a prediction. Explainer: we use LIME to explain which regions are most important for the classification model to make a prediction. The LIME method outputs the top-5 regions that are most important. Explainer: The regions that are not depicted are less important than depicted ones. Seeker: Alright, I understand it better now. Seeker: How was swin transformer selected as a image classification model. Is it the leading model or is it heavily peer reviewed? Explainer: swin transfomer performs well on image-related tasks. It is one of the leading models in image classification tasks and many other image-related tasks. Explainer: It can reach over 84% accuracy on a very large image classification dataset (imagenet) Seeker: Is the swin transformer model able to identify accurately within the 1000 predefined categories when there are artifacts / noises in the images? For example, does the addition of leaves in the image Explainer: From observations of other experiments, the model is robust to artifacts/noises. But I'm not sure how well it is. Seeker: I see, do you happen to know how the model distinquish between outputs. Lets say you place a goldfish and dog within the same visual input. Does it take the one that occupies the majority space? Explainer: The model will have a high probability to classify the image into the dog category or the goldfish category Explainer: When we observe how the image is classified into the dog category, a good classification model should focus on the area of dogs. Explainer: On the other hand, when we observe how the image is classified into the goldfish category, a good classification model should focus on the area of goldfish. Seeker: I see, interesting. Previously, you mentioned that the picture depicting the explanation for the model prediction shows the top-5 regions. If you were to show only that area, would the model still be able to predict it is a leopard? Or doesit still need the whole picture"
} | {
"output": "Explainer: it will depend on the weight of these top-5 regions. If the top-5 regions contain enough features to classify the category, then the classification model can make the right decision"
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: does it identify the specific type of animal instead of the general identification? Explainer: This will depend on the dataset used to train the classification model. The model here can identify specific type of animal. Explainer: Like different species of fish, cats, or dogs. Seeker: ohh thats kinda amazing Seeker: is it possible for it to detect multiple animals at once? Seeker: what is the accuracy level? Explainer: For the model on the left side, it can only detect one animal at once. But as long as we have data, we can train the model to detect multiple animals at once. Explainer: The accuracy is 84% on very large dataset (imagenet) Seeker: ohh Explainer: Even though the classification model performs pretty well, how the classification model predicts a category for an image is not understandable to humans. So we have the Grad-cam to explain the behavior of the classification model. Seeker: oh, understand Explainer: Grad-cam generates a heatmap that highlights the regions of the image that are most important for the prediction, which is the second image on the left screen. Explainer: Do you have questions about the Grad-cam method? Seeker: can this method be used on humans next time for identification or tracking etc.? Seeker: or specifically for animals only?"
} | {
"output": "Explainer: This method is applied to certain types of deep learning models. Do you mean use Grad-cam to explain deep learning models for detecting humans?"
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: So to summarise up about the description, the grad-CAM which generates a heatmap is able to identify the image/object in the picture using deep learning model? Explainer: Not exactly. There is a deep learning model that is used to classify an image into a category. Explainer: This is the classification model. Explainer: The first image is the input of the classification model. The classification mode classifies it as a goldfish. Explainer: But the classification model is complex. We don't understand it. We don't know how it predicts the image as a goldfish. Explainer: So we use grad-cam to explain which region the classification model focuses on to make such decision Seeker: ohhhh i get it now Seeker: but the grad-CAM which generates a heatmap shows high importance (indicated in red), could be anything. how would the grad-cam be able to detect the classification model Seeker: would the grad-CAM in any sense, detect wrongly and give a wrong classification/answer? Explainer: The heatmap generated by Grad-cam is based on the classification model. Explainer: The classification model will extract some features from original images. Explainer: Then the grad-cam generates the heatmap by calculating how changes in these features will affect the classification model's prediction. Explainer: it could be possible. Seeker: so the grad-cam is not a 100% method proof right Explainer: Yes. Seeker: in that case, how would the grad-cam help to assist in like progressing of any research Explainer: Enough though it is not 100% correct, the heatmap generated by Grad-cam highly depends on the classification model. Explainer: It can help users understand why a model made a specific prediction and may increase users' trust in the classification models. Explainer: For research, it can help them debugs the model. Explainer: Like, if the classification model made a wrong decision, we may use grad-cam to observe why it made such a wrong decision, is it because it focuses on the wrong areas of images. Seeker: okay okay Explainer: The researcher can those explanations from grad-cam to update the model. Seeker: so all in all, it is also able to help be a predictive and preventive kind of analysis Explainer: Yes. Explainer: It helps us better understand the \"black box\" deep learning model. Seeker: okay"
} | {
"output": "Explainer: Do you have other questions?"
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: how did u decide on which part of the image has the heatmap Explainer: The explanation method, Grad-CAM, generate the heatmap by calculating how changes to each region of the image would affect the classification model's output Seeker: what is the probability for an error of prediction Explainer: If changes of an image region will highly affect the model's prediction, that image region will be redder. Explainer: For the classification model, the accuracy is 84%. So the probability of error is 16%. Seeker: what if there are overlapping features of other objects, will it be able to tell both objects Explainer: It depends. If objects' unique visual characters are not hidden by others, the classification model may identify them. Explainer: But the classification model's results will depend on the data it sees in the training data and also depends on what it learns from the data. Seeker: ok Seeker: am I supposed to keep on asking questions ? Explainer: Yes. Can you ask more questions? Seeker: will the background of the image affect the recognition of the object if the background contains similar unique features as the image Explainer: Yes. It's possible. As the classification model will automatically learn unique features for a category from data, we don't know what parts of the image the classification model will focus on to make a prediction. Explainer: Sometimes it may focus on the wrong area to make a prediction. Explainer: No matter the prediction is correct or not. Seeker: ok Explainer: With the explanation, grad-cam, we can observe which regions the classification model focuses on to make a prediction. We can debug the model based on the explanation. Seeker: there is also a possibility that insufficient data is fed to the training data leading to a false prediction ? Seeker: as some objects might be unique in different ways Explainer: Yes, exactly. insufficient data can lead to false predictions. Seeker: am I asking the questions in the right direction Seeker: I feel like I just throwing random questions"
} | {
"output": "Explainer: For example, if there is not enough data, a classification model may use the appearance of water to predict the goldfish. Very good questions. You can ask any questions. You can also ask questions about the explanation method."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: does it identify the specific type of animal instead of the general identification? Explainer: This will depend on the dataset used to train the classification model. The model here can identify specific type of animal. Explainer: Like different species of fish, cats, or dogs. Seeker: ohh thats kinda amazing Seeker: is it possible for it to detect multiple animals at once? Seeker: what is the accuracy level? Explainer: For the model on the left side, it can only detect one animal at once. But as long as we have data, we can train the model to detect multiple animals at once. Explainer: The accuracy is 84% on very large dataset (imagenet) Seeker: ohh Explainer: Even though the classification model performs pretty well, how the classification model predicts a category for an image is not understandable to humans. So we have the Grad-cam to explain the behavior of the classification model. Seeker: oh, understand Explainer: Grad-cam generates a heatmap that highlights the regions of the image that are most important for the prediction, which is the second image on the left screen. Explainer: Do you have questions about the Grad-cam method? Seeker: can this method be used on humans next time for identification or tracking etc.? Seeker: or specifically for animals only? Explainer: This method is applied to certain types of deep learning models. Explainer: Do you mean use Grad-cam to explain deep learning models for detecting humans? Seeker: identifying as in the identify of the person for tracking Explainer: Sorry, I didn't understand you questions. Explainer: Can you rephrase your last question? Seeker: In the case of someone who were to shoplift, can a deep learning model be used to identify a person's identity (for tracking)?"
} | {
"output": "Explainer: Technically, it is possible for a deep learning model to be trained to identify specific individuals. However, the effectiveness of such a system can vary greatly. The model's ability to accurately identify individuals depends on the quality and quantity of the data it has been trained on."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: what is swin transformer Seeker: explain lime Explainer: It is the classification model we used to classify an image into a category. It performs well on image-related tasks. Explainer: For the example on the left screen, the classification model, swin transformer, classifies the first image as a leopard. Seeker: why is the explanation inaccurate Explainer: LIME is a method to explain a specific prediction of a deep learning model by highlighting the most important features among input features. Seeker: what happens when there are more than 1 animal Explainer: Using the example on the left screen, the input is an image and the output is class Leopard. The highlighted part of the original image is the reason why the classification model predicts the input image into a class leopard. Explainer: Sorry, Can you specify this question? Seeker: the explanation does not highlight all the parts of the leopard, why Explainer: The classification model will have a high probability to classify the image into all animals. Explainer: When we observe how the image is classified into one animal, a good model should focus on the area of that animal. Explainer: On the other hand, when we observe how the image is classified into another animal, a good classification model should focus on the area of another animal. Seeker: can lime be used without internet"
} | {
"output": "Explainer: The classification model will use the entire image to make a prediction. But it is very complex and we don't understand how it makes such a prediction. So we use LIME to explain which regions are most important for the classification model to make a prediction. The LIME method outputs the top-5 regions that are most important. Yes. It can. That's why the explanation didn't highlight all the parts of the leopard"
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: with small parts of the picture only seen by AI, how would you ensure that the small parts would make up the desired picture Explainer: The classification model will see the entire image to make a prediction. Seeker: for example, if AI only takes in the leopard spots on certain area, it could be from a picture of a lady wearing leopard spots Explainer: The small parts highlighted by the explanation model show the regions that are most important for the classification to make a prediction. Seeker: so we show a picture of a leopard and the output by AI will state that it is a leopard. what if the input is someone wearing full leopard spots clothing Seeker: based on the model prediction only taking small parts of the input picture. it could possibly only take in the leopard spots Explainer: This will be another task. The output of someone wearing full leopard spots clothing will depends on the label we tell the model. If we tells the model this is a lady and feed the model with many examples, the model will output lady category. Seeker: I see. so basically AI machine learning through multiple inputs like those captcha Explainer: Here the LIME wants to explain if the classification model classifies this image into a leopard, which regions of the input are most important for the model to make such a prediction. Explainer: Yes. The AI model will learn from data. Explainer: We don't know what they learned and what they focus on to make a decision. Seeker: alright. that's understandable. But large samples are required. What about the recent issue about AI thinking that humans eat spaghetti with hands? like those AI generated photos"
} | {
"output": "Explainer: The LIME method aims to help humans better understand the behavior of AI models."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: What is the simpler model of the LIME model based on? Explainer: An example simpler model is the linear model. Seeker: How are regions of image that are most important for model's prediction chosen?"
} | {
"output": "Explainer: The output is the sum of input features with weights. The weights be used to indicate the importance of input features, i.e., image regions"
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: what is swin transformer? Explainer: It is the classification model we used to classify an image into a category. It performs well on image-related tasks. Explainer: For the example on the left screen, the classification model, swin transformer, classifies the first image as a leopard. Seeker: Are there other classification models, like the three choices I saw earlier? Explainer: Yes. There are many classification models, like Alexnet, Vgg16. Explainer: They have different accuracy. Seeker: Swin transformer has the highest accuracy out of all the models? Explainer: It's not the best model. There are classification models performing better than swin transformer. But it performs better than AlexNet or VGG16. Explainer: But swin transformer can reach 84% accuracy on a very large dataset. Explainer: Do you have any questions about the explanation method, LIME? Seeker: What is the explanation model used for? Explainer: The LIME model is used to explain the classification model. Explainer: The classification model is a deep learning model, which is very complex to understand for humans. Explainer: Explanation models, like LIME we used here, are used to explain how the classification model makes such a prediction. Explainer: It generates explanations by highlighting regions of an image that are most important for the classification model's prediction. This is what shows in the second image. Seeker: How Lime model recognises the most important parts for the model prediction Explainer: The LIME model works by training a local surrogate model around a specific prediction. Explainer: The surrogate model is a simple, understandable model. Explainer: The simple surrogate model is used to mimic the behavior of the complex AI model around one data point. Seeker: From the images i've seen earlier, are the other 2 images/options also explained using LIME Explainer: Yes. Three options are all explained by LIME. Explainer: There are three different classifications model in three options, but their predictions are explained by the same explanation method, LIME. Explainer: Usually better classification model will have a more reasonable explanation. Seeker: how about the precision of the model"
} | {
"output": "Explainer: Do you mean the classification model or the explanation model?"
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: with small parts of the picture only seen by AI, how would you ensure that the small parts would make up the desired picture Explainer: The classification model will see the entire image to make a prediction. Seeker: for example, if AI only takes in the leopard spots on certain area, it could be from a picture of a lady wearing leopard spots Explainer: The small parts highlighted by the explanation model show the regions that are most important for the classification to make a prediction. Seeker: so we show a picture of a leopard and the output by AI will state that it is a leopard. what if the input is someone wearing full leopard spots clothing Seeker: based on the model prediction only taking small parts of the input picture. it could possibly only take in the leopard spots Explainer: This will be another task. The output of someone wearing full leopard spots clothing will depends on the label we tell the model. If we tells the model this is a lady and feed the model with many examples, the model will output lady category. Seeker: I see. so basically AI machine learning through multiple inputs like those captcha"
} | {
"output": "Explainer: Here the LIME wants to explain if the classification model classifies this image into a leopard, which regions of the input are most important for the model to make such a prediction. Yes. The AI model will learn from data. We don't know what they learned and what they focus on to make a decision."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: with small parts of the picture only seen by AI, how would you ensure that the small parts would make up the desired picture Explainer: The classification model will see the entire image to make a prediction. Seeker: for example, if AI only takes in the leopard spots on certain area, it could be from a picture of a lady wearing leopard spots Explainer: The small parts highlighted by the explanation model show the regions that are most important for the classification to make a prediction. Seeker: so we show a picture of a leopard and the output by AI will state that it is a leopard. what if the input is someone wearing full leopard spots clothing Seeker: based on the model prediction only taking small parts of the input picture. it could possibly only take in the leopard spots Explainer: This will be another task. The output of someone wearing full leopard spots clothing will depends on the label we tell the model. If we tells the model this is a lady and feed the model with many examples, the model will output lady category. Seeker: I see. so basically AI machine learning through multiple inputs like those captcha Explainer: Here the LIME wants to explain if the classification model classifies this image into a leopard, which regions of the input are most important for the model to make such a prediction. Explainer: Yes. The AI model will learn from data. Explainer: We don't know what they learned and what they focus on to make a decision. Seeker: alright. that's understandable. But large samples are required. What about the recent issue about AI thinking that humans eat spaghetti with hands? like those AI generated photos Explainer: The LIME method aims to help humans better understand the behavior of AI models. Seeker: I see. Explainer: Maybe the AI model sees someone eating spaghetti with hands when the model is trained. Explainer: But I'm not sure about this. Seeker: I think it is more of babies eating spaghetti with hands which made AI have the connection of humans eating spaghetti with hands. Would you say that this is a failure in the AI machine learning? or would you say that more models are required Explainer: The AI model is designed to think about the world in a statistical way . Seeker: uh huh Explainer: I cannot say it is a failure. But there would have a better way to design the model in the future. Seeker: With better prediction I guess. There's also an issue where AI has issues with skin tones Seeker: like back then there was this issue with apple phones where the face prediction does not acknowledge people with darker skin? Explainer: Yes. it happens Explainer: It will have differents problems when facing the complex world. Seeker: and would you say that more models will be able to help with this in the future? Explainer: In my view, it will be. Seeker: I see. Alright. Hopefully it works Seeker: how do i proceed to the next step?"
} | {
"output": "Explainer: There are lots of researchers in the world working hard to make AI models better. Thanks for your questions."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: Why is the model prediction only part of the leopard's face? Explainer: The classification model's prediction is the image contains a leopard. Seeker: Why are only certain parts of the picture highlighted? Explainer: The second image is the explanation of this model prediction. It tries to explain which region the classification model focuses on to make such decision Seeker: Why is this model better than other state of the art models? Explainer: It means the highlighted parts are the most important regions thatmake the classification model classifies this image into the leopard. Explainer: Do you mean the explanation model or the classification model? Seeker: Ah okay I understand now. The LIME model is the explanation model Explainer: yes. Seeker: How does it work then? How will it choose which data points to highlight?"
} | {
"output": "Explainer: LIME works by training a local surrogate model around a specific prediction. The surrogate model is a white box model and inherently interpretable. An example surrogate model is the linear model. The output is the sum of input features with weights. The weights be used to indicate the importance of input features."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: what is swin transformer? Explainer: It is the classification model we used to classify an image into a category. It performs well on image-related tasks. Explainer: For the example on the left screen, the classification model, swin transformer, classifies the first image as a leopard. Seeker: Are there other classification models, like the three choices I saw earlier? Explainer: Yes. There are many classification models, like Alexnet, Vgg16. Explainer: They have different accuracy. Seeker: Swin transformer has the highest accuracy out of all the models? Explainer: It's not the best model. There are classification models performing better than swin transformer. But it performs better than AlexNet or VGG16. Explainer: But swin transformer can reach 84% accuracy on a very large dataset. Explainer: Do you have any questions about the explanation method, LIME? Seeker: What is the explanation model used for? Explainer: The LIME model is used to explain the classification model. Explainer: The classification model is a deep learning model, which is very complex to understand for humans. Explainer: Explanation models, like LIME we used here, are used to explain how the classification model makes such a prediction. Explainer: It generates explanations by highlighting regions of an image that are most important for the classification model's prediction. This is what shows in the second image. Seeker: How Lime model recognises the most important parts for the model prediction"
} | {
"output": "Explainer: The LIME model works by training a local surrogate model around a specific prediction. The surrogate model is a simple, understandable model. The simple surrogate model is used to mimic the behavior of the complex AI model around one data point."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: Why uuse blue and red? Explainer: Because redder colors are usually associated with warmth and high energy while bluer colors are associated with coolness and low energy. Explainer: That is decided by the authors of the method. It's also okay to use other color pairs in the heatmap. Seeker: Why present it using a heatmap ? Explainer: This visual representation allows both technical and non-technical individuals to understand what parts of an image are contributing most to a model's prediction Explainer: A heatmap is like a colorful overlay on the picture that helps you see what the classification model is focusing on. Seeker: Tell me more about the GRAD-cam method. Explainer: sure Explainer: Grad-CAM is a technique to generate explanations by highlighting regions of an image that are most important for a classification model's prediction. Seeker: Can this method be applied to non living things as well? Explainer: It works by calculating the derivative of the output class with respect to the feature maps in the final convolutional layer of the classification model and using these derivatives to weight the feature maps. Explainer: It depends on the dataset that is used to train the classification model. Explainer: Here the classification model can also predict non-living objects in the image. Seeker: Can it also predict the heat map of supernatural beings like ghosts as well? Explainer: Sorry, currently cannot. But if have some data about ghosts, you can train the model to recognize it. Seeker: Do you have the means and technology to collect data for ghosts?"
} | {
"output": "Explainer: Sorry, I don't know. Do you have other questions?"
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: why is it classified under swin transformer?\nwhat does it mean? Explainer: Swin transformer is the model name of the classification model, which classifies an input image into a category. Explainer: For the example on the left screen, the classification model, swin transformer, classifies the first image as a leopard. Seeker: why is the area around the eye and nose used to come up with the prediction? Explainer: the highlighted may contain visual characters that are unique to the category Explainer: Like the eye and nose shown in the second image. Explainer: The explanation image is generated by another model called, LIME. Seeker: can you explain more about LIME ? Explainer: Sure. Seeker: in this scenario? Explainer: LIME is a method to interpret a specific prediction of a deep learning model by highlighting the most important features among input features. Seeker: how does it pick from from the image of eyes and nose and pick it out as a leopard? Explainer: Here is the thing. We have a classification model at first. It classifies an image as a leopard. But the classification model is very complex, we don't know how it makes such a prediction. It learns to make predictions automatically from data. Seeker: does the machine learning have intelligence of many images of 1000 leopards and then able to pick out the correct classificaiton baed on the data points of the eye and nose? Explainer: So we use LIME model to explain which regions are most important for the classification model when it makes a prediction. Seeker: ok Explainer: The most important regions are highlighted in the second image. Seeker: what is the algoithm? it means lime? Explainer: An algorithm is a step-by-step procedure or set of rules for performing a specific task or solving a particular problem. Explainer: LIME is an algorithm. The classification model, swin transformer is also an algorithm Seeker: ok Explainer: The Machine learning model will learn to get a prediction for an input. We don't know what it will focus on. Seeker: what is deep neural networks? Explainer: But when it makes the prediction of the image on the left screen, it focuses more on the eye and nose area, as explained by LIME. Explainer: Deep neural networks are a type of artificial intelligence model that are designed to solve different tasks. Seeker: ok i dont have more questions"
} | {
"output": "Explainer: Thanks for your questions."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: does the Grad-CAM retroactively explain the explanation based on deep learning output? Explainer: Sorry, what do you mean by retroactively? Explainer: Grad-cam will generate explanations after a learning model makes a prediction. Seeker: oh I mean the explanation is generated after the fact (i.e., the classification is already done, and Grad-CAM tries to find out why the model carries out its classification in a certain way) Seeker: Yep I think that's retroactive! Explainer: Yes. that's how Grad-cam works. Seeker: thanks! Seeker: what does this sentence mean: This heatmap is generated by weighting the activations of the final convolutional layer by their corresponding gradients and averaging the resulting weights spatially. Seeker: particularly, the term \"final convolutional layer\" Explainer: The classification model will contain many layers to extract features from the image. the convolutional layer is one of them. Seeker: what other layers are there?"
} | {
"output": "Explainer: Grad-CAM works by analyzing the output of the final convolutional layer of the network and calculating how changes to each region of the image would affect the network's output."
} |
{
"input": "Explainer: hello, you can ask me any questions to help you better understand the explanation on the left screen. Seeker: hello, does the LIME model depend on the size of the object of the image analyzed or it is just going to pick up distinct features of the object? Explainer: LIME will segments the image into a set of superpixels (i.e., contiguous pixels that are similar to each other) Explainer: Then it will find the top 5 super-pixels that are important to the model prediction. Seeker: ok can. may i ask if the LIME model is better than the other models? and how are they similar? Explainer: There are different explanation methods, e.g., Grad-cam or Integrated gradients. They can all explain model's prediction. But it's hard to universally say one model will be better than others. Seeker: What is the classification model and what is the explanation model? Explainer: The classification model classify the visual input into a class. Explainer: For the example on the left screen, the classification model classify the input image into the leopard class. Explainer: and the explanation model (LIME) tries to explain how the classification model classifies the visual input into a specific class by generating the second image on the left screen. Seeker: Okay. I see. Why do the yellow edges in the explanation image appear jagged Explainer: That's because the image is first grouped into super-pixels. It is based on the similarity between closed pixels. So it may be not very smooth. Seeker: I don't have any more questions."
} | {
"output": "Explainer: okay. Thanks for your questions."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: what is the image classification model Explainer: It is a deep learning model that can classify an image into a category Explainer: For the example on the left screen, the classification model classifies the first image as goldfish. Seeker: can you tell me a little more about the swin transformer?"
} | {
"output": "Explainer: Sure. The Swin Transformer is a type of deep learning model designed to analyze images, which works by dividing the image into small parts and paying selective attention to each part, thereby allowing it to handle high-quality, detailed images more efficiently. swin transformer performs well on image-related tasks, like the image classification here."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: What am I supposed to do here? Explainer: Ask questions about the explanation on the left screen. Seeker: There isn’t much that can be seen from the explanation for the model prediction"
} | {
"output": "Explainer: Can specify more about this?"
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: Why is the model prediction only part of the leopard's face? Explainer: The classification model's prediction is the image contains a leopard. Seeker: Why are only certain parts of the picture highlighted? Explainer: The second image is the explanation of this model prediction. It tries to explain which region the classification model focuses on to make such decision Seeker: Why is this model better than other state of the art models? Explainer: It means the highlighted parts are the most important regions thatmake the classification model classifies this image into the leopard. Explainer: Do you mean the explanation model or the classification model? Seeker: Ah okay I understand now. The LIME model is the explanation model Explainer: yes. Seeker: How does it work then? How will it choose which data points to highlight? Explainer: LIME works by training a local surrogate model around a specific prediction. Explainer: The surrogate model is a white box model and inherently interpretable. Explainer: An example surrogate model is the linear model. Explainer: The output is the sum of input features with weights. The weights be used to indicate the importance of input features. Seeker: so the surrogate model will be trained on the prediction outputs of the transformer used to predict the image? Seeker: ah okay understand Explainer: yes. the surrogate model will be trained on the prediction outputs of the transformer Seeker: ok understood, thanks!"
} | {
"output": "Explainer: Do you have other questions?"
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: does it identify the specific type of animal instead of the general identification? Explainer: This will depend on the dataset used to train the classification model. The model here can identify specific type of animal. Explainer: Like different species of fish, cats, or dogs. Seeker: ohh thats kinda amazing Seeker: is it possible for it to detect multiple animals at once? Seeker: what is the accuracy level? Explainer: For the model on the left side, it can only detect one animal at once. But as long as we have data, we can train the model to detect multiple animals at once. Explainer: The accuracy is 84% on very large dataset (imagenet) Seeker: ohh Explainer: Even though the classification model performs pretty well, how the classification model predicts a category for an image is not understandable to humans. So we have the Grad-cam to explain the behavior of the classification model. Seeker: oh, understand Explainer: Grad-cam generates a heatmap that highlights the regions of the image that are most important for the prediction, which is the second image on the left screen. Explainer: Do you have questions about the Grad-cam method? Seeker: can this method be used on humans next time for identification or tracking etc.? Seeker: or specifically for animals only? Explainer: This method is applied to certain types of deep learning models. Explainer: Do you mean use Grad-cam to explain deep learning models for detecting humans? Seeker: identifying as in the identify of the person for tracking Explainer: Sorry, I didn't understand you questions. Explainer: Can you rephrase your last question? Seeker: In the case of someone who were to shoplift, can a deep learning model be used to identify a person's identity (for tracking)? Explainer: Technically, it is possible for a deep learning model to be trained to identify specific individuals. Explainer: However, the effectiveness of such a system can vary greatly. The model's ability to accurately identify individuals depends on the quality and quantity of the data it has been trained on. Seeker: oh okay Seeker: understand Explainer: Do you have other questions? Seeker: nope i dont have for now"
} | {
"output": "Explainer: Thanks for your questions."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: how does the model know that it is a leopard even though from the explanation, it is only part of the face? Explainer: The classification model will use the entire image to make a prediction. Explainer: But it is very complex and we don't understand how it makes such a prediction. Explainer: So we use LIME to explain which regions are most important for the classification model to make a prediction. Explainer: The LIME method outputs the top-5 regions that are most important. Explainer: That's why only parts of the image are shown. Seeker: how then does it \"evolve\" or get better? Explainer: The classification model automatically learn from the data fed to it. Explainer: We can feed it with more data. The model will automatically optimize itself based on the data. Explainer: or we can use LIME explanation method to find bias and to improve the model Explainer: Using image classification as an example, if a model consistently assigns higher attribution scores to the background regions when classifying an image into the leopard category, this could indicate the model focused on the wrong features to decide the leopard category. Seeker: icic\nnoted Seeker: based on the current image on the left for explanation, it doesnt cover the entire leopard face\ndoes that mean in the future, it require lesser image to make prediction?\nmeaning if it is an image of a leopard hiding behind leaves, it can detect that it is a leopard? Explainer: if we showed the model only the areas that LIME indicates as being most important, there's no guarantee that the classification model would still predict correctly. Explainer: This is because while these regions are important, they may be not necessarily sufficient for making a correct prediction. The model often needs the context provided by the entire image. Seeker: what do you mean by context? Explainer: Like the leopard on the left screen. The model may need the entire image to know this is an animal but not someone wearing a cloth with a leopard spot. Seeker: ah ok\nunderstand Seeker: how do you foresee this technology being commercialized? Explainer: \"meaning if it is an image of a leopard hiding behind leaves, it can detect that it is a leopard?\" This is possible. If a classification model can focus on the right areas of the image, it can detect the leopard hiding behind leaves. Explainer: I'm not sure about this. The LIME method still has some problems. But I think people will increase their requirements toward the explanation of deep learning model's predictions. Seeker: does deep learning of image and text work the same way? Explainer: especially in some domains, such as healthcare, finance, and legal systems, where understanding the reasons behind a deep learning model’s prediction can impact critical decision-making processes. Explainer: Yes. The LIME method can be used to explain image-input and text-input models Seeker: something like you can feed it MRI or Xray and it can detect the problems? Explainer: Yes. The deep learning model can detect disease from the MRI or X-ray. And explanation models can provide reasons for the deep learning model's predictions. Through the explanations, doctors or patients can choose whether to believe the decision of deep learning models. Seeker: icic\nnoted\n\nthanks for sharing Explainer: Do you have other questions? Seeker: not for now"
} | {
"output": "Explainer: Thanks for your questions."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: how does the model know that it is a leopard even though from the explanation, it is only part of the face? Explainer: The classification model will use the entire image to make a prediction. Explainer: But it is very complex and we don't understand how it makes such a prediction. Explainer: So we use LIME to explain which regions are most important for the classification model to make a prediction. Explainer: The LIME method outputs the top-5 regions that are most important. Explainer: That's why only parts of the image are shown. Seeker: how then does it \"evolve\" or get better? Explainer: The classification model automatically learn from the data fed to it. Explainer: We can feed it with more data. The model will automatically optimize itself based on the data. Explainer: or we can use LIME explanation method to find bias and to improve the model Explainer: Using image classification as an example, if a model consistently assigns higher attribution scores to the background regions when classifying an image into the leopard category, this could indicate the model focused on the wrong features to decide the leopard category. Seeker: icic\nnoted Seeker: based on the current image on the left for explanation, it doesnt cover the entire leopard face\ndoes that mean in the future, it require lesser image to make prediction?\nmeaning if it is an image of a leopard hiding behind leaves, it can detect that it is a leopard?"
} | {
"output": "Explainer: if we showed the model only the areas that LIME indicates as being most important, there's no guarantee that the classification model would still predict correctly. This is because while these regions are important, they may be not necessarily sufficient for making a correct prediction. The model often needs the context provided by the entire image."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen Seeker: How does the simpler model work? Explainer: For an input data point, we can perturb around this data point and get predictions from complex classification models. Then we use these data samples to train the simple surrogate model. Therefore the simpler model can mimic the behavior of the complex model around one data point Explainer: As we understand the behavior of the simpler model, we can know the importance of each image region when the simpler model does a prediction. Seeker: What metrics do you use to measure in the model? Colours? Pixels? Explainer: Do you mean how we measure the classification model? Seeker: Yes Explainer: We measure the classification model by the accuracy of predicting correct labels. Seeker: How many images do you need in order to train a data set?"
} | {
"output": "Explainer: It will depend on the model size and the complexity of the task. For the image classification model here, it is trained with 1.2 million labeled images."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: what is the factor in the explanation for the model prediction that indicated that it was a leopard Explainer: Here as we can observe from the second image, the explanation model highlight some regions with yellow line to explain to us how the classification model get its prediction. Seeker: Ok... Explainer: The classification model is very complex. We cannot directly understand how it predicts the image as a leopard. Explainer: So we use an explanation model, LIME, to try to explain how the classification model gets the prediction. Explainer: And the explanation is the second image. Seeker: Ok, so in summary the LIME framework helps to identify what the image is? Explainer: In summary, the LIME method explain the prediction from a deep learning classification model Explainer: The deep learning classification model predicts or identifies what the image is. Seeker: Would it be possible to identify different people, using the deep learning classification model? Seeker: If the AI were fed the data Explainer: Yes. We can do it with corresponding data. Seeker: What is swim transformer then?"
} | {
"output": "Explainer: Swin transformer is the classification model, which an classify an image into a category. Swin transformer is a very complex model and contains many neural layers, e.g., convolutional layers, and transformer layers. it can classify an image into a category. But we cannot know which regions of the image it focuses on to do a prediction."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: what is the factor in the explanation for the model prediction that indicated that it was a leopard"
} | {
"output": "Explainer: Here as we can observe from the second image, the explanation model highlight some regions with yellow line to explain to us how the classification model get its prediction."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: What am I supposed to do here? Explainer: Ask questions about the explanation on the left screen. Seeker: There isn’t much that can be seen from the explanation for the model prediction Explainer: Can specify more about this? Seeker: A leopard is defined by its spots, but in the explanation for the model prediction there are only a few spots covered, while spaces that should be occupied by spots are occupied by an eye and nose"
} | {
"output": "Explainer: The classification model will learn how to decide what the object is mainly based on the data. It will learn automatically from data. It may focus on different features that we humans focus on."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: What is the simpler model of the LIME model based on? Explainer: An example simpler model is the linear model. Seeker: How are regions of image that are most important for model's prediction chosen? Explainer: The output is the sum of input features with weights. The weights be used to indicate the importance of input features, i.e., image regions Seeker: What are the constraints of the LIME model? What are cases that it may be inaccurate in classifying? Seeker: How are the weights determined? Explainer: For an input data point, we can perturb around this data point and get predictions from the complex models. Then we use these data samples to train the simpler model. Explainer: Therefore the simpler model can mimic the behavior of the complex model around one data point. Explainer: As we need to do perturbations around input data points, the LIME method may be unstable. Also, the type of simpler models also will affect the quality of explanations. Seeker: How would you explain the term \"perturbations\" to a five year old? Seeker: Are there any models similar to LIME model, and what makes the LIME model different from similar models? What may make the LIME model more suitable as compated to similar models? Seeker: *compared Explainer: Perturbations of an image are like tiny scribbles on a picture. Explainer: There are many other explanation methods, like shap or grad-cam. Explainer: The LIME method can be applied to any type of deep learning model. Some other methods are model-specific, which means they can only be applied to a certain type of deep learning model. Seeker: Does that mean the perturbations are like \"distractions\" that the computer has to be trained to filter against to be able to catergorise accurately? Explainer: Not exactly. Explainer: the purpose of the perturbations is not really to train the classification model, but to understand it better. Explainer: We want to see which changes or 'distractions' cause the model to misidentify the image, because that tells us a lot about how the model is making its decisions. Explainer: So these perturbations are like little experiments that help us learn what the model considers important or unimportant in the images it's looking at. Seeker: Ah I see, thanks. Explainer: Do you have other questions? Seeker: Yep, just one more! Seeker: Typing Explainer: okay. sure. Seeker: Given that the LIME model prioritises regions of image that are most important for prediction, would it be possible someday that it can highlight the entire portion that makes up the determined subject, or would only more important sections being highlighted be deemed sufficient explanation? Seeker: ^Hope the above question kinda make sense Explainer: In theory, it's possible that LIME could highlight the entire portion of an image that makes up the determined subject if every part of that subject was equally important in making the prediction. But in practice, not every part of an image is equally important for making a prediction. Explainer: For instance, when recognizing a leopard, the model might give more weight to the eyes, and nose, and less weight to the ears. So LIME might only highlight these more important parts. Seeker: I see! Alright, no more questions that I can think of currently."
} | {
"output": "Explainer: Thanks for your questions."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: Are regions colored in red therefore areas that have been identified as containing key features for the animal in question? Explainer: Yes. it means the classification model focuses more on those area to make a prediction. Explainer: as explained by the Grad-cam method. Seeker: The explanation provided is pretty clear! Thank you. Explainer: thanks. Explainer: Can you ask more questions? Explainer: As our study aims to collect more questions from users. Seeker: How was the deep learning conducted? Explainer: We use the swin transformer model here. We trained it on a large dataset that contains many image-category pairs. Seeker: Was there a specific range of success rate that the researchers aim to achieve when generating the model? Explainer: The classification model will learn to give a prediction of categories based on the input image. Seeker: I see, that's interesting. How well does the machine fare on animals cross-breeds between 2 animals that are previously featured in the dataset? Explainer: researchers will want the classification model to be as accurate and robust as possible. And want the explanation model to be as faithful as possible. Explainer: I don't know about this. We actually cannot understand how the classification model do a prediction. Explainer: We don't know what the classification model learned for each category. Explainer: Therefore, we have the explanation model that tries to explain which region the classification model focuses on to make a prediction. Seeker: How many convolutional layers are there in this method and what does each layer consist of?"
} | {
"output": "Explainer: I'm not sure about the exact number of convolutional layers in the swin transformer model. A convolutional layer contains several kernels to extract features from the input."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: What am I supposed to do here? Explainer: Ask questions about the explanation on the left screen. Seeker: There isn’t much that can be seen from the explanation for the model prediction Explainer: Can specify more about this? Seeker: A leopard is defined by its spots, but in the explanation for the model prediction there are only a few spots covered, while spaces that should be occupied by spots are occupied by an eye and nose Explainer: The classification model will learn how to decide what the object is mainly based on the data. It will learn automatically from data. Explainer: It may focus on different features that we humans focus on. Seeker: Interesting"
} | {
"output": "Explainer: But as the classification model is quite complex. We don't know which regions the classification model focuses on to make a prediction. Is it the same as what we humans think? Or it will focus on other regions."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: what do you mean by importance? Seeker: for the different colours Seeker: what is the final convolutional layer Explainer: The red color means the area is of high importance. The blue color means the area is of low importance. The importance means how important the region is when a classification model makes a prediction. Explainer: High important area means the image in these area contains key features for the classification model to classify an image into a category. Explainer: It is a layer in the classification model. The output of that layer is high-dimensional features extracted by the classificaiton model. Explainer: Grad-cam can only be used for deep learning models that have convolutional layers. Seeker: I see Seeker: what is a gradcam Explainer: Grad-CAM is a technique to generate explanations by highlighting regions of an image that are most important for a deep neural network's prediction. Explainer: Here grad-cam model generates the heatmap to explain the prediction from a classification model. Seeker: So the Grad-cam is used to explain the classfication network? Explainer: Yes. exactly. Seeker: what is a heatmap Seeker: What is a deep learning model? any examples?"
} | {
"output": "Explainer: The heatmap is the red-blue graph on your left screen. It is used to highlight the regions of the image that are most important for the classification model's prediction. Bluer colors are used to represent areas of low importance, while redder colors indicate areas of high importance."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: what do you mean by importance? Seeker: for the different colours Seeker: what is the final convolutional layer Explainer: The red color means the area is of high importance. The blue color means the area is of low importance. The importance means how important the region is when a classification model makes a prediction. Explainer: High important area means the image in these area contains key features for the classification model to classify an image into a category. Explainer: It is a layer in the classification model. The output of that layer is high-dimensional features extracted by the classificaiton model. Explainer: Grad-cam can only be used for deep learning models that have convolutional layers. Seeker: I see Seeker: what is a gradcam Explainer: Grad-CAM is a technique to generate explanations by highlighting regions of an image that are most important for a deep neural network's prediction. Explainer: Here grad-cam model generates the heatmap to explain the prediction from a classification model. Seeker: So the Grad-cam is used to explain the classfication network? Explainer: Yes. exactly. Seeker: what is a heatmap Seeker: What is a deep learning model? any examples? Explainer: The heatmap is the red-blue graph on your left screen. It is used to highlight the regions of the image that are most important for the classification model's prediction. Explainer: Bluer colors are used to represent areas of low importance, while redder colors indicate areas of high importance. Seeker: ok Explainer: A deep learning model is a type of artificial intelligence that learns from data to make predictions or decisions, like, the classification model can classify an image into a category, the face recognition model can recognize different people's faces, and the language model can answer your questions. Explainer: Do you have other questions? Seeker: no"
} | {
"output": "Explainer: Thanks for your questions."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: what is grad-cam?"
} | {
"output": "Explainer: Grad-cam is the name of the explanation method. It is used to explain the prediction from the classification model There is a classification model that can classify an image into a category. Here it classifies the image as a goldfish. But the classification model is complex. We don't understand it. We don't know how it predicts the image as a goldfish. Then we use the grad-cam, a explanation model to generate the second image, which tries to explain which region the classification model focuses on to make such decision."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: So to summarise up about the description, the grad-CAM which generates a heatmap is able to identify the image/object in the picture using deep learning model? Explainer: Not exactly. There is a deep learning model that is used to classify an image into a category. Explainer: This is the classification model. Explainer: The first image is the input of the classification model. The classification mode classifies it as a goldfish. Explainer: But the classification model is complex. We don't understand it. We don't know how it predicts the image as a goldfish. Explainer: So we use grad-cam to explain which region the classification model focuses on to make such decision Seeker: ohhhh i get it now Seeker: but the grad-CAM which generates a heatmap shows high importance (indicated in red), could be anything. how would the grad-cam be able to detect the classification model Seeker: would the grad-CAM in any sense, detect wrongly and give a wrong classification/answer? Explainer: The heatmap generated by Grad-cam is based on the classification model. Explainer: The classification model will extract some features from original images. Explainer: Then the grad-cam generates the heatmap by calculating how changes in these features will affect the classification model's prediction. Explainer: it could be possible. Seeker: so the grad-cam is not a 100% method proof right Explainer: Yes. Seeker: in that case, how would the grad-cam help to assist in like progressing of any research Explainer: Enough though it is not 100% correct, the heatmap generated by Grad-cam highly depends on the classification model. Explainer: It can help users understand why a model made a specific prediction and may increase users' trust in the classification models. Explainer: For research, it can help them debugs the model. Explainer: Like, if the classification model made a wrong decision, we may use grad-cam to observe why it made such a wrong decision, is it because it focuses on the wrong areas of images. Seeker: okay okay Explainer: The researcher can those explanations from grad-cam to update the model. Seeker: so all in all, it is also able to help be a predictive and preventive kind of analysis"
} | {
"output": "Explainer: Yes. It helps us better understand the \"black box\" deep learning model."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: Why is the model prediction only part of the leopard's face? Explainer: The classification model's prediction is the image contains a leopard. Seeker: Why are only certain parts of the picture highlighted? Explainer: The second image is the explanation of this model prediction. It tries to explain which region the classification model focuses on to make such decision Seeker: Why is this model better than other state of the art models? Explainer: It means the highlighted parts are the most important regions thatmake the classification model classifies this image into the leopard. Explainer: Do you mean the explanation model or the classification model? Seeker: Ah okay I understand now. The LIME model is the explanation model"
} | {
"output": "Explainer: yes."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: How does the method arrive at the prediction based solely on the generated heatmap? Explainer: The classification model will use the entire image to make a prediction. But it is very complex and we don't understand how it makes such a prediction. Explainer: We use grad-cam to explanation the model's prediction by highlighing regions of an image that are most important for a deep neural network's prediction. Explainer: So heatmap is used to explain how a classification model makes a prediction after the classification model has made an prediction. Seeker: I don't fully understand the terms on how the heatmap is generated. Is it possible to explain in more layman terms? Explainer: Yes. sure. Explainer: The classification model will extract features from images and use those images to make a prediction. Explainer: Grad-CAM works by calculating how changes to each feature would affect the network's output. Explainer: The more sensitive the classification model is to a region of the image, the more important that region is, and redder that region will be. Seeker: Ok, thanks. Seeker: What does the image classification model 'swim transformer' mean? Explainer: Swin transformer is the classification model we used to classify an image into a category. It performs well on image-related tasks. Explainer: For the example on the left screen, the classification model, swin transformer, classifies the first image as the category of goldfish. Seeker: Does that mean there are other classification models that can also be used to classify these images? Explainer: Yes. There are many models that can be used to classify images. Seeker: How is it determined which model is the most suitable to use for certain images?"
} | {
"output": "Explainer: The classification model will be tested on a dataset. We choose the model based on accuracy."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: So to summarise up about the description, the grad-CAM which generates a heatmap is able to identify the image/object in the picture using deep learning model? Explainer: Not exactly. There is a deep learning model that is used to classify an image into a category. Explainer: This is the classification model. Explainer: The first image is the input of the classification model. The classification mode classifies it as a goldfish. Explainer: But the classification model is complex. We don't understand it. We don't know how it predicts the image as a goldfish. Explainer: So we use grad-cam to explain which region the classification model focuses on to make such decision Seeker: ohhhh i get it now Seeker: but the grad-CAM which generates a heatmap shows high importance (indicated in red), could be anything. how would the grad-cam be able to detect the classification model Seeker: would the grad-CAM in any sense, detect wrongly and give a wrong classification/answer?"
} | {
"output": "Explainer: The heatmap generated by Grad-cam is based on the classification model. The classification model will extract some features from original images. Then the grad-cam generates the heatmap by calculating how changes in these features will affect the classification model's prediction. it could be possible."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: So to summarise up about the description, the grad-CAM which generates a heatmap is able to identify the image/object in the picture using deep learning model? Explainer: Not exactly. There is a deep learning model that is used to classify an image into a category. Explainer: This is the classification model. Explainer: The first image is the input of the classification model. The classification mode classifies it as a goldfish. Explainer: But the classification model is complex. We don't understand it. We don't know how it predicts the image as a goldfish. Explainer: So we use grad-cam to explain which region the classification model focuses on to make such decision Seeker: ohhhh i get it now Seeker: but the grad-CAM which generates a heatmap shows high importance (indicated in red), could be anything. how would the grad-cam be able to detect the classification model Seeker: would the grad-CAM in any sense, detect wrongly and give a wrong classification/answer? Explainer: The heatmap generated by Grad-cam is based on the classification model. Explainer: The classification model will extract some features from original images. Explainer: Then the grad-cam generates the heatmap by calculating how changes in these features will affect the classification model's prediction. Explainer: it could be possible. Seeker: so the grad-cam is not a 100% method proof right Explainer: Yes. Seeker: in that case, how would the grad-cam help to assist in like progressing of any research"
} | {
"output": "Explainer: Enough though it is not 100% correct, the heatmap generated by Grad-cam highly depends on the classification model. It can help users understand why a model made a specific prediction and may increase users' trust in the classification models. For research, it can help them debugs the model. Like, if the classification model made a wrong decision, we may use grad-cam to observe why it made such a wrong decision, is it because it focuses on the wrong areas of images."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: What am I supposed to do here? Explainer: Ask questions about the explanation on the left screen. Seeker: There isn’t much that can be seen from the explanation for the model prediction Explainer: Can specify more about this? Seeker: A leopard is defined by its spots, but in the explanation for the model prediction there are only a few spots covered, while spaces that should be occupied by spots are occupied by an eye and nose Explainer: The classification model will learn how to decide what the object is mainly based on the data. It will learn automatically from data. Explainer: It may focus on different features that we humans focus on. Seeker: Interesting Explainer: But as the classification model is quite complex. We don't know which regions the classification model focuses on to make a prediction. Is it the same as what we humans think? Or it will focus on other regions. Seeker: From this experience it seems that the focus is on other regions Explainer: So we have an explanation method, called LIME here. We use this explanation method to draw which regions the classification model focuses on when it makes a prediction. Seeker: The AI maybe sees the leopard as a whole rather than just the spots Explainer: Yes. Explainer: Probably. Seeker: Is this technique a recently developed one? Explainer: This method was developed several years ago. Seeker: It’s an interesting technique. So how else can it explain the predictions? Explainer: LIME can explain tabular data, text data, and image data, providing explanations in all these cases. Explainer: Also, it is a model-agnostic method, which means it can explain different types of deep learning models. Seeker: What other kind of learning models are there?"
} | {
"output": "Explainer: Like deep learning models used to automatically diagnose disease and identify the sentiment of a sentence. or self-driving car. The explanation method like LIME, is particularly effective in complex domains where interpretability is crucial, such as healthcare, finance, and legal systems. Because understanding the reasons behind a deep learning model’s prediction can impact critical decision-making processes."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Explainer: Simple and easy questions are also encouraged. Seeker: does this model prediction only applies to animals? Explainer: It can also classify other objects, like baby chairs or football helmet Seeker: is this like Google Lens? Explainer: yes, the classification model here can do similar task as Google Lens Explainer: But the classification model is usually very complex. Explainer: It's hard to be understood by humans. We don't know how it predicts the image as a leopard. Explainer: So we have the explanation method, grad-cam. It generates the second image, which tries to explain which region the classification model focuses on to make such a decision Seeker: i see. ok thanks! i dont have any other questions Explainer: Can you ask more questions? Seeker: can i know the limitations of LIME then Explainer: As the LIME need to do perturbations around input data points, the LIME method may be unstable. Also, the type of surrogate models in LIME also will affect the quality of explanations. Seeker: then how would u increase the credibility and stability of this method? Explainer: We can sample more data points around the input data and carefully choose the surrogate models. Explainer: Do you have other questions? Seeker: ok thanks i do not have any more questions"
} | {
"output": "Explainer: Thanks for your questions."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: How does the method arrive at the prediction based solely on the generated heatmap? Explainer: The classification model will use the entire image to make a prediction. But it is very complex and we don't understand how it makes such a prediction. Explainer: We use grad-cam to explanation the model's prediction by highlighing regions of an image that are most important for a deep neural network's prediction. Explainer: So heatmap is used to explain how a classification model makes a prediction after the classification model has made an prediction. Seeker: I don't fully understand the terms on how the heatmap is generated. Is it possible to explain in more layman terms?"
} | {
"output": "Explainer: Yes. sure. The classification model will extract features from images and use those images to make a prediction. Grad-CAM works by calculating how changes to each feature would affect the network's output. The more sensitive the classification model is to a region of the image, the more important that region is, and redder that region will be."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: Can you define \"swim transformer\" Explainer: It is the classification model we used to classify an image into a category. It performs well on image-related tasks. Explainer: For the example on the left screen, the classification model, swin transformer, classifies the first image as a leopard. Seeker: I see, what abou the picture depicting the explanation for the model prediction. Are the areas which are not voided help the model identify why the image is a leopard?"
} | {
"output": "Explainer: The classification model will use the entire image to make a prediction. we use LIME to explain which regions are most important for the classification model to make a prediction. The LIME method outputs the top-5 regions that are most important. The regions that are not depicted are less important than depicted ones."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: does it identify the specific type of animal instead of the general identification? Explainer: This will depend on the dataset used to train the classification model. The model here can identify specific type of animal. Explainer: Like different species of fish, cats, or dogs. Seeker: ohh thats kinda amazing Seeker: is it possible for it to detect multiple animals at once? Seeker: what is the accuracy level? Explainer: For the model on the left side, it can only detect one animal at once. But as long as we have data, we can train the model to detect multiple animals at once. Explainer: The accuracy is 84% on very large dataset (imagenet) Seeker: ohh"
} | {
"output": "Explainer: Even though the classification model performs pretty well, how the classification model predicts a category for an image is not understandable to humans. So we have the Grad-cam to explain the behavior of the classification model."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: what is the image classification model Explainer: It is a deep learning model that can classify an image into a category Explainer: For the example on the left screen, the classification model classifies the first image as goldfish. Seeker: can you tell me a little more about the swin transformer? Explainer: Sure. The Swin Transformer is a type of deep learning model designed to analyze images, which works by dividing the image into small parts and paying selective attention to each part, thereby allowing it to handle high-quality, detailed images more efficiently. Explainer: swin transformer performs well on image-related tasks, like the image classification here. Seeker: is it right to say that the Grad-CAM method is more of a visualization method for the data processing by the Swin Transformer? Explainer: Yes. The grad-CAM method visualized how the Swin Transformer makes a prediction of an image. Seeker: can you explain the following sentence in simpler terms? \"This heatmap is generated by weighting the activations of the final convolutional layer by their corresponding gradients and averaging the resulting weights spatially.\" Explainer: The swin transformer has many neural layers, which can extract features of images. the convolutional layer is one of these neural layers. The activations of the convolutional layer contains the features for each image regions. Explainer: Grad-cam generate the second heatmap image by calculating how changes to feature of each image region would affect the model's output. Explainer: When we change a region of the image and the model's output changes a lot, this means that image region is important to the model prediction. Explainer: Bluer colors are used to represent areas of low importance, while redder colors indicate areas of high importance. Seeker: does that mean that brighter colors in the actual image equate to changes in the image region? Explainer: No. When changing images, we will equally change feature of image regions. Seeker: so how do you weight the activations of the convolutional layer?"
} | {
"output": "Explainer: activations of the convolutional layer contain extracted features of images. For each region of images, there are multiple features. We will calculate how changes in each feature for each image region will affect the models prediction."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: what is the image classification model Explainer: It is a deep learning model that can classify an image into a category Explainer: For the example on the left screen, the classification model classifies the first image as goldfish. Seeker: can you tell me a little more about the swin transformer? Explainer: Sure. The Swin Transformer is a type of deep learning model designed to analyze images, which works by dividing the image into small parts and paying selective attention to each part, thereby allowing it to handle high-quality, detailed images more efficiently. Explainer: swin transformer performs well on image-related tasks, like the image classification here. Seeker: is it right to say that the Grad-CAM method is more of a visualization method for the data processing by the Swin Transformer?"
} | {
"output": "Explainer: Yes. The grad-CAM method visualized how the Swin Transformer makes a prediction of an image."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: what is swin transformer Seeker: explain lime"
} | {
"output": "Explainer: It is the classification model we used to classify an image into a category. It performs well on image-related tasks. For the example on the left screen, the classification model, swin transformer, classifies the first image as a leopard."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: Hi, is it possible to generate the heatmap based on only a photo?"
} | {
"output": "Explainer: The heatmap is generated based on the importance of image regions to the classification model. The classification model is a deep learning model that can classify an image into a category. It can classify the first image into the goldfish category. But the classification model is very complex to understand for humans. Explanation models, like Grad-cam we used here, are used to explain how the classification model makes such a prediction.\nIt generates explanations by highlighting regions of an image that are most important for the classification model's prediction. This is the heatmap shown in the second image."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: What is the simpler model of the LIME model based on? Explainer: An example simpler model is the linear model. Seeker: How are regions of image that are most important for model's prediction chosen? Explainer: The output is the sum of input features with weights. The weights be used to indicate the importance of input features, i.e., image regions Seeker: What are the constraints of the LIME model? What are cases that it may be inaccurate in classifying? Seeker: How are the weights determined? Explainer: For an input data point, we can perturb around this data point and get predictions from the complex models. Then we use these data samples to train the simpler model. Explainer: Therefore the simpler model can mimic the behavior of the complex model around one data point. Explainer: As we need to do perturbations around input data points, the LIME method may be unstable. Also, the type of simpler models also will affect the quality of explanations. Seeker: How would you explain the term \"perturbations\" to a five year old? Seeker: Are there any models similar to LIME model, and what makes the LIME model different from similar models? What may make the LIME model more suitable as compated to similar models? Seeker: *compared Explainer: Perturbations of an image are like tiny scribbles on a picture. Explainer: There are many other explanation methods, like shap or grad-cam. Explainer: The LIME method can be applied to any type of deep learning model. Some other methods are model-specific, which means they can only be applied to a certain type of deep learning model. Seeker: Does that mean the perturbations are like \"distractions\" that the computer has to be trained to filter against to be able to catergorise accurately? Explainer: Not exactly. Explainer: the purpose of the perturbations is not really to train the classification model, but to understand it better. Explainer: We want to see which changes or 'distractions' cause the model to misidentify the image, because that tells us a lot about how the model is making its decisions. Explainer: So these perturbations are like little experiments that help us learn what the model considers important or unimportant in the images it's looking at. Seeker: Ah I see, thanks. Explainer: Do you have other questions? Seeker: Yep, just one more! Seeker: Typing"
} | {
"output": "Explainer: okay. sure."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: Can you define \"swim transformer\" Explainer: It is the classification model we used to classify an image into a category. It performs well on image-related tasks. Explainer: For the example on the left screen, the classification model, swin transformer, classifies the first image as a leopard. Seeker: I see, what abou the picture depicting the explanation for the model prediction. Are the areas which are not voided help the model identify why the image is a leopard? Explainer: The classification model will use the entire image to make a prediction. Explainer: we use LIME to explain which regions are most important for the classification model to make a prediction. The LIME method outputs the top-5 regions that are most important. Explainer: The regions that are not depicted are less important than depicted ones. Seeker: Alright, I understand it better now. Seeker: How was swin transformer selected as a image classification model. Is it the leading model or is it heavily peer reviewed?"
} | {
"output": "Explainer: swin transfomer performs well on image-related tasks. It is one of the leading models in image classification tasks and many other image-related tasks. It can reach over 84% accuracy on a very large image classification dataset (imagenet)"
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: how does the model know that it is a leopard even though from the explanation, it is only part of the face? Explainer: The classification model will use the entire image to make a prediction. Explainer: But it is very complex and we don't understand how it makes such a prediction. Explainer: So we use LIME to explain which regions are most important for the classification model to make a prediction. Explainer: The LIME method outputs the top-5 regions that are most important. Explainer: That's why only parts of the image are shown. Seeker: how then does it \"evolve\" or get better? Explainer: The classification model automatically learn from the data fed to it. Explainer: We can feed it with more data. The model will automatically optimize itself based on the data. Explainer: or we can use LIME explanation method to find bias and to improve the model Explainer: Using image classification as an example, if a model consistently assigns higher attribution scores to the background regions when classifying an image into the leopard category, this could indicate the model focused on the wrong features to decide the leopard category. Seeker: icic\nnoted Seeker: based on the current image on the left for explanation, it doesnt cover the entire leopard face\ndoes that mean in the future, it require lesser image to make prediction?\nmeaning if it is an image of a leopard hiding behind leaves, it can detect that it is a leopard? Explainer: if we showed the model only the areas that LIME indicates as being most important, there's no guarantee that the classification model would still predict correctly. Explainer: This is because while these regions are important, they may be not necessarily sufficient for making a correct prediction. The model often needs the context provided by the entire image. Seeker: what do you mean by context? Explainer: Like the leopard on the left screen. The model may need the entire image to know this is an animal but not someone wearing a cloth with a leopard spot. Seeker: ah ok\nunderstand Seeker: how do you foresee this technology being commercialized? Explainer: \"meaning if it is an image of a leopard hiding behind leaves, it can detect that it is a leopard?\" This is possible. If a classification model can focus on the right areas of the image, it can detect the leopard hiding behind leaves. Explainer: I'm not sure about this. The LIME method still has some problems. But I think people will increase their requirements toward the explanation of deep learning model's predictions. Seeker: does deep learning of image and text work the same way? Explainer: especially in some domains, such as healthcare, finance, and legal systems, where understanding the reasons behind a deep learning model’s prediction can impact critical decision-making processes. Explainer: Yes. The LIME method can be used to explain image-input and text-input models Seeker: something like you can feed it MRI or Xray and it can detect the problems?"
} | {
"output": "Explainer: Yes. The deep learning model can detect disease from the MRI or X-ray. And explanation models can provide reasons for the deep learning model's predictions. Through the explanations, doctors or patients can choose whether to believe the decision of deep learning models."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: How does the LIME model sample data points/regions when interpreting an input image Explainer: It will first group the original image into super-pixels. Explainer: A superpixel is a group of connected pixels in an image that share a similar color. Explainer: we can perturb the pixel value of superpixel to get sampled data points. Seeker: I see, thanks that is very clear. Seeker: Once the model selects super pixels, how does it prioritise which super pixels to accept or reject when making predictions of the more complex model? How many rounds of decisions will it make? Explainer: LIME works by training a local surrogate model around a specific prediction. The surrogate model is a white box model and inherently interpretable. Explainer: We will use sampled data points to train the surrogate model. Explainer: An example surrogate model is the linear model. Explainer: The output is the sum of input features with weights. The weights be used to indicate the importance of input features, i.e., the super-pixels. Explainer: we output the top 5 super-pixels that are important to the model prediction. Explainer: That's how we get the second image. Seeker: ok, I think I understand! Explainer: Do you have other questions? Seeker: no, I think it is very clear. very educational - thank you"
} | {
"output": "Explainer: Thanks for your questions."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: what is the factor in the explanation for the model prediction that indicated that it was a leopard Explainer: Here as we can observe from the second image, the explanation model highlight some regions with yellow line to explain to us how the classification model get its prediction. Seeker: Ok..."
} | {
"output": "Explainer: The classification model is very complex. We cannot directly understand how it predicts the image as a leopard. So we use an explanation model, LIME, to try to explain how the classification model gets the prediction. And the explanation is the second image."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: what is the image classification model Explainer: It is a deep learning model that can classify an image into a category Explainer: For the example on the left screen, the classification model classifies the first image as goldfish. Seeker: can you tell me a little more about the swin transformer? Explainer: Sure. The Swin Transformer is a type of deep learning model designed to analyze images, which works by dividing the image into small parts and paying selective attention to each part, thereby allowing it to handle high-quality, detailed images more efficiently. Explainer: swin transformer performs well on image-related tasks, like the image classification here. Seeker: is it right to say that the Grad-CAM method is more of a visualization method for the data processing by the Swin Transformer? Explainer: Yes. The grad-CAM method visualized how the Swin Transformer makes a prediction of an image. Seeker: can you explain the following sentence in simpler terms? \"This heatmap is generated by weighting the activations of the final convolutional layer by their corresponding gradients and averaging the resulting weights spatially.\" Explainer: The swin transformer has many neural layers, which can extract features of images. the convolutional layer is one of these neural layers. The activations of the convolutional layer contains the features for each image regions. Explainer: Grad-cam generate the second heatmap image by calculating how changes to feature of each image region would affect the model's output. Explainer: When we change a region of the image and the model's output changes a lot, this means that image region is important to the model prediction. Explainer: Bluer colors are used to represent areas of low importance, while redder colors indicate areas of high importance. Seeker: does that mean that brighter colors in the actual image equate to changes in the image region? Explainer: No. When changing images, we will equally change feature of image regions. Seeker: so how do you weight the activations of the convolutional layer? Explainer: activations of the convolutional layer contain extracted features of images. For each region of images, there are multiple features. Explainer: We will calculate how changes in each feature for each image region will affect the models prediction. Seeker: what are some features? Explainer: That's automatically learned by the classification model. Seeker: would you classify the model as a black box? Explainer: The classification model uses these features to understand the image. Explainer: Yes. Explainer: The classification model is black box model. Seeker: what are some limitations of the Grad-CAM method? Explainer: Therefore we need the explanation method, like Grad-cam here to understand how the black box model makes a prediction. Explainer: It cannot be applied to all types of deep learning models. It can only be used for deep learning models that have convolutional layers. Seeker: can you list other visualization methods? Explainer: Yes. there are other explanation methods, like, LIME, Integrated gradients, and SHAP. Seeker: why is the Grad-CAM method selected over these other explanation methods? Explainer: It's easier to understand when using it to explain the predictions of images. But we also have explanations generated by other methods. Explainer: As I can't send pictures to you, I cannot show explanations generated by other methods. Seeker: why was the swin transformer model used in this scenario over other neural models?"
} | {
"output": "Explainer: It performs well here. It can reach 84% accuracy in a large dataset."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: Can you define \"swim transformer\" Explainer: It is the classification model we used to classify an image into a category. It performs well on image-related tasks. Explainer: For the example on the left screen, the classification model, swin transformer, classifies the first image as a leopard. Seeker: I see, what abou the picture depicting the explanation for the model prediction. Are the areas which are not voided help the model identify why the image is a leopard? Explainer: The classification model will use the entire image to make a prediction. Explainer: we use LIME to explain which regions are most important for the classification model to make a prediction. The LIME method outputs the top-5 regions that are most important. Explainer: The regions that are not depicted are less important than depicted ones. Seeker: Alright, I understand it better now. Seeker: How was swin transformer selected as a image classification model. Is it the leading model or is it heavily peer reviewed? Explainer: swin transfomer performs well on image-related tasks. It is one of the leading models in image classification tasks and many other image-related tasks. Explainer: It can reach over 84% accuracy on a very large image classification dataset (imagenet) Seeker: Is the swin transformer model able to identify accurately within the 1000 predefined categories when there are artifacts / noises in the images? For example, does the addition of leaves in the image"
} | {
"output": "Explainer: From observations of other experiments, the model is robust to artifacts/noises. But I'm not sure how well it is."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: Are regions colored in red therefore areas that have been identified as containing key features for the animal in question?"
} | {
"output": "Explainer: Yes. it means the classification model focuses more on those area to make a prediction. as explained by the Grad-cam method."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: what is swin transformer Seeker: explain lime Explainer: It is the classification model we used to classify an image into a category. It performs well on image-related tasks. Explainer: For the example on the left screen, the classification model, swin transformer, classifies the first image as a leopard. Seeker: why is the explanation inaccurate Explainer: LIME is a method to explain a specific prediction of a deep learning model by highlighting the most important features among input features. Seeker: what happens when there are more than 1 animal Explainer: Using the example on the left screen, the input is an image and the output is class Leopard. The highlighted part of the original image is the reason why the classification model predicts the input image into a class leopard. Explainer: Sorry, Can you specify this question? Seeker: the explanation does not highlight all the parts of the leopard, why Explainer: The classification model will have a high probability to classify the image into all animals. Explainer: When we observe how the image is classified into one animal, a good model should focus on the area of that animal. Explainer: On the other hand, when we observe how the image is classified into another animal, a good classification model should focus on the area of another animal. Seeker: can lime be used without internet Explainer: The classification model will use the entire image to make a prediction. But it is very complex and we don't understand how it makes such a prediction. So we use LIME to explain which regions are most important for the classification model to make a prediction. The LIME method outputs the top-5 regions that are most important. Explainer: Yes. It can. Explainer: That's why the explanation didn't highlight all the parts of the leopard Seeker: how does lime compare with other similiar software Explainer: The LIME method can be applied to any type of deep learning model. Some other methods are model-specific, which means they can only be applied to a certain type of deep learning model. Explainer: But LIME also has some drawbacks, e.g., it may be unstable. Also, the type of surrogate models used in the LIME method also will affect the quality of explanations. Seeker: has lime been used on a mass scale before Seeker: will this be used for survaillance"
} | {
"output": "Explainer: Can you specify more about this question?"
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: What animals are more easy to read? Explainer: Animals that have more features that others don't have. Explainer: But it also depends on the classification model and the dataset used to train the classification model. Seeker: Ok thanks Explainer: Can you ask more questions, especially about the explanation method? Seeker: How accurate is heatmap that highlights the important regions of the image for the prediction. Seeker: And what’s the % accuracy of Grad-CAM method Explainer: We cannot measure the accuracy of the Grad-CAM method as we don't have the ground truth of how the classification model makes a prediction. Explainer: But in general, it works well. Seeker: How do u weighting the activations of the final convolutional layer by their corresponding gradients"
} | {
"output": "Explainer: It will calculate how changes to each region of the image would affect the classification model's output. If the model's output is sensitive to some areas of the input images, it means those areas are important for the model's prediction."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: what is the factor in the explanation for the model prediction that indicated that it was a leopard Explainer: Here as we can observe from the second image, the explanation model highlight some regions with yellow line to explain to us how the classification model get its prediction. Seeker: Ok... Explainer: The classification model is very complex. We cannot directly understand how it predicts the image as a leopard. Explainer: So we use an explanation model, LIME, to try to explain how the classification model gets the prediction. Explainer: And the explanation is the second image. Seeker: Ok, so in summary the LIME framework helps to identify what the image is? Explainer: In summary, the LIME method explain the prediction from a deep learning classification model Explainer: The deep learning classification model predicts or identifies what the image is. Seeker: Would it be possible to identify different people, using the deep learning classification model? Seeker: If the AI were fed the data Explainer: Yes. We can do it with corresponding data. Seeker: What is swim transformer then? Explainer: Swin transformer is the classification model, which an classify an image into a category. Explainer: Swin transformer is a very complex model and contains many neural layers, e.g., convolutional layers, and transformer layers. Explainer: it can classify an image into a category. But we cannot know which regions of the image it focuses on to do a prediction. Seeker: how advanced is the swim transformer -- if the predefined categories are races and the visual input was a person of mixed decent would it be able to categorise Seeker: sure, thanks for explaining"
} | {
"output": "Explainer: The swin transformer is pretty accurate in a very large dataset. It can reach 84% on that dataset. But I'm not sure whether it can perform well in the case you mentioned."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: What animals are more easy to read? Explainer: Animals that have more features that others don't have. Explainer: But it also depends on the classification model and the dataset used to train the classification model. Seeker: Ok thanks Explainer: Can you ask more questions, especially about the explanation method? Seeker: How accurate is heatmap that highlights the important regions of the image for the prediction. Seeker: And what’s the % accuracy of Grad-CAM method Explainer: We cannot measure the accuracy of the Grad-CAM method as we don't have the ground truth of how the classification model makes a prediction. Explainer: But in general, it works well. Seeker: How do u weighting the activations of the final convolutional layer by their corresponding gradients Explainer: It will calculate how changes to each region of the image would affect the classification model's output. Explainer: If the model's output is sensitive to some areas of the input images, it means those areas are important for the model's prediction. Seeker: Ok thanks Seeker: What other explanations method are there other than grad-cam Explainer: There are some other explanation methods, like, LIME, integrated gradients and SHAP. Seeker: What made u choose grad-cam instead of the rest Explainer: This is an example. In this example, we use Grad-cam to explain the classification model's prediction. You can also choose to use other explanation methods to explain the classification model's predictions. But due to technical limitations, I cannot show you now. Explainer: Do you have other questions? Seeker: No more other qns"
} | {
"output": "Explainer: Thanks for your questions."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: What about ai using image based recognition by utilising through experience Explainer: Sorry, I don't understand your question. Seeker: Or through training sets and test sets to determine the reliability of the ai"
} | {
"output": "Explainer: The classification model is evaluated by the test set. But evaluating a classification model on test sets can only let humans know how accurate the model is."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: what is grad-cam? Explainer: Grad-cam is the name of the explanation method. Explainer: It is used to explain the prediction from the classification model Explainer: There is a classification model that can classify an image into a category. Here it classifies the image as a goldfish. Explainer: But the classification model is complex. We don't understand it. We don't know how it predicts the image as a goldfish. Explainer: Then we use the grad-cam, a explanation model to generate the second image, which tries to explain which region the classification model focuses on to make such decision. Seeker: Interesting Seeker: In the event that there is more than 1 important region in an image (eg the image has a goldfish and a shark), will the grad-cam be able to detect both creatures? Explainer: Grad-cam actually cannot detect features. It is used to explain the prediction of the classification model, which is swin transformer here. Explainer: But if you mean whether the swin transformer can detect both creatures, it may do it. Seeker: Sorry, the heatmap I mean since the resulting heatmap will overlay on the original image to provide a visual representation Explainer: A good classification model should assign high probability to both shark and goldfish if an image contains both of them. Explainer: If we use the grad cam to explain how the classification model classifies the image into the shark class, a good classification model will focus on the features related to the shark. Explainer: it's the same for the goldfish. Seeker: What does it mean by \"weighting the activations of the final convolutional layer by their corresponding gradients\"? Does that refers to the colour gradient of the image? Explainer: The classification model will first extract features from original images, and generate a feature map. Then we will calculate how changes to each feature would affect the network's output. Explainer: The feature map here is \"activations of the final convolutional layer\" Explainer: if the prediction is sensitive to some features in the feature map, this means those part of the feature is of high importance Explainer: it will be redder in the heatmap. Seeker: Understand Seeker: Does the grad-cam method have a high probability of correct predictions?"
} | {
"output": "Explainer: we cannot measure its correctness as there is no ground truth for the explanation of the classification model. But It works pretty well in general. Swin transformer predicts quite accurately. around 84% on a very large dataset (i.e., imagenet.)"
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: does the Grad-CAM retroactively explain the explanation based on deep learning output? Explainer: Sorry, what do you mean by retroactively? Explainer: Grad-cam will generate explanations after a learning model makes a prediction. Seeker: oh I mean the explanation is generated after the fact (i.e., the classification is already done, and Grad-CAM tries to find out why the model carries out its classification in a certain way) Seeker: Yep I think that's retroactive!"
} | {
"output": "Explainer: Yes. that's how Grad-cam works."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Explainer: Simple and easy questions are also encouraged. Seeker: does this model prediction only applies to animals?"
} | {
"output": "Explainer: It can also classify other objects, like baby chairs or football helmet"
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: hello, what does 'weighting the activations of the final convolutional layer by their corresponding gradients and averaging the resulting weights spatially' mean? Explainer: The classification model will extract some features from input images. Explainer: This is the the activations of the final convolutional layer. Explainer: Then Grad-cam calculates how changes to each feature would affect the classification model's output. Explainer: If the model's prediction is sensitive to some features, it means those features are of high importance. Seeker: how does the model know what features to extract? Explainer: The classification model will automatically learn from the data. Seeker: what makes the model sensitive to certain features?"
} | {
"output": "Explainer: If the model relies more on certain features, little change in those features will affect the model's prediction."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen Seeker: How does the simpler model work? Explainer: For an input data point, we can perturb around this data point and get predictions from complex classification models. Then we use these data samples to train the simple surrogate model. Therefore the simpler model can mimic the behavior of the complex model around one data point Explainer: As we understand the behavior of the simpler model, we can know the importance of each image region when the simpler model does a prediction. Seeker: What metrics do you use to measure in the model? Colours? Pixels? Explainer: Do you mean how we measure the classification model? Seeker: Yes Explainer: We measure the classification model by the accuracy of predicting correct labels. Seeker: How many images do you need in order to train a data set? Explainer: It will depend on the model size and the complexity of the task. For the image classification model here, it is trained with 1.2 million labeled images. Seeker: Is this image classification tool going to be used by someone well verse with AI and it's terms? Seeker: Because I feel that the explanation can be quite complex for someone that is not well versed in it Explainer: For the classification model, anyone can use it. We input an image to the model, the model will output it's prediction about the image. Explainer: For the explanation method, it is quite complex for someone that is not well versed in it Explainer: Here, you can ask any questions about the explanation method. Seeker: Yes, I took some time to understand the text that I was reading hahaha Seeker: How accurate is this model? Explainer: The classification model here can reach 84% accuracy. Explainer: For the explanation method, as we don't have the ground truth of how the classification model makes a prediction, we cannot measure the accuracy of the LIME method. Seeker: I see Seeker: Are there other models that classify the image? Is LIME just one of them?"
} | {
"output": "Explainer: The LIME model is actually not the classification model. The classification model is the swin transformer, which can classify an image into a category. The LIME method is an explanation method, which explains the classification model's behavior after the classification model made a prediction The classification model is a very complex deep learning model. We don't understand how it works to make a prediction."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: How does the LIME model sample data points/regions when interpreting an input image Explainer: It will first group the original image into super-pixels. Explainer: A superpixel is a group of connected pixels in an image that share a similar color. Explainer: we can perturb the pixel value of superpixel to get sampled data points. Seeker: I see, thanks that is very clear. Seeker: Once the model selects super pixels, how does it prioritise which super pixels to accept or reject when making predictions of the more complex model? How many rounds of decisions will it make?"
} | {
"output": "Explainer: LIME works by training a local surrogate model around a specific prediction. The surrogate model is a white box model and inherently interpretable. We will use sampled data points to train the surrogate model. An example surrogate model is the linear model. The output is the sum of input features with weights. The weights be used to indicate the importance of input features, i.e., the super-pixels. we output the top 5 super-pixels that are important to the model prediction. That's how we get the second image."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: Why uuse blue and red? Explainer: Because redder colors are usually associated with warmth and high energy while bluer colors are associated with coolness and low energy. Explainer: That is decided by the authors of the method. It's also okay to use other color pairs in the heatmap. Seeker: Why present it using a heatmap ? Explainer: This visual representation allows both technical and non-technical individuals to understand what parts of an image are contributing most to a model's prediction Explainer: A heatmap is like a colorful overlay on the picture that helps you see what the classification model is focusing on. Seeker: Tell me more about the GRAD-cam method."
} | {
"output": "Explainer: sure Grad-CAM is a technique to generate explanations by highlighting regions of an image that are most important for a classification model's prediction."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: with small parts of the picture only seen by AI, how would you ensure that the small parts would make up the desired picture Explainer: The classification model will see the entire image to make a prediction. Seeker: for example, if AI only takes in the leopard spots on certain area, it could be from a picture of a lady wearing leopard spots Explainer: The small parts highlighted by the explanation model show the regions that are most important for the classification to make a prediction. Seeker: so we show a picture of a leopard and the output by AI will state that it is a leopard. what if the input is someone wearing full leopard spots clothing Seeker: based on the model prediction only taking small parts of the input picture. it could possibly only take in the leopard spots"
} | {
"output": "Explainer: This will be another task. The output of someone wearing full leopard spots clothing will depends on the label we tell the model. If we tells the model this is a lady and feed the model with many examples, the model will output lady category."
} |
{
"input": "Explainer: hello, you can ask me any questions to help you better understand the explanation on the left screen. Seeker: hello, does the LIME model depend on the size of the object of the image analyzed or it is just going to pick up distinct features of the object? Explainer: LIME will segments the image into a set of superpixels (i.e., contiguous pixels that are similar to each other) Explainer: Then it will find the top 5 super-pixels that are important to the model prediction. Seeker: ok can. may i ask if the LIME model is better than the other models? and how are they similar? Explainer: There are different explanation methods, e.g., Grad-cam or Integrated gradients. They can all explain model's prediction. But it's hard to universally say one model will be better than others. Seeker: What is the classification model and what is the explanation model? Explainer: The classification model classify the visual input into a class. Explainer: For the example on the left screen, the classification model classify the input image into the leopard class. Explainer: and the explanation model (LIME) tries to explain how the classification model classifies the visual input into a specific class by generating the second image on the left screen. Seeker: Okay. I see. Why do the yellow edges in the explanation image appear jagged"
} | {
"output": "Explainer: That's because the image is first grouped into super-pixels. It is based on the similarity between closed pixels. So it may be not very smooth."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: How does the LIME model sample data points/regions when interpreting an input image Explainer: It will first group the original image into super-pixels. Explainer: A superpixel is a group of connected pixels in an image that share a similar color. Explainer: we can perturb the pixel value of superpixel to get sampled data points. Seeker: I see, thanks that is very clear. Seeker: Once the model selects super pixels, how does it prioritise which super pixels to accept or reject when making predictions of the more complex model? How many rounds of decisions will it make? Explainer: LIME works by training a local surrogate model around a specific prediction. The surrogate model is a white box model and inherently interpretable. Explainer: We will use sampled data points to train the surrogate model. Explainer: An example surrogate model is the linear model. Explainer: The output is the sum of input features with weights. The weights be used to indicate the importance of input features, i.e., the super-pixels. Explainer: we output the top 5 super-pixels that are important to the model prediction. Explainer: That's how we get the second image. Seeker: ok, I think I understand!"
} | {
"output": "Explainer: Do you have other questions?"
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: what is swim transformer Explainer: The Swin Transformer is a type of artificial intelligence model designed to analyze images, which works by dividing the image into small parts and paying selective attention to each part, thereby allowing it to handle high-quality, detailed images more efficiently. Explainer: Here we use the swin transformer to classify an image into a category. Seeker: what is LIME"
} | {
"output": "Explainer: LIME is a method to explain a specific prediction of a deep learning model by highlighting the most important features among input features. Here we use LIME to explain a prediction from swin transformer."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: What am I supposed to do here? Explainer: Ask questions about the explanation on the left screen. Seeker: There isn’t much that can be seen from the explanation for the model prediction Explainer: Can specify more about this? Seeker: A leopard is defined by its spots, but in the explanation for the model prediction there are only a few spots covered, while spaces that should be occupied by spots are occupied by an eye and nose Explainer: The classification model will learn how to decide what the object is mainly based on the data. It will learn automatically from data. Explainer: It may focus on different features that we humans focus on. Seeker: Interesting Explainer: But as the classification model is quite complex. We don't know which regions the classification model focuses on to make a prediction. Is it the same as what we humans think? Or it will focus on other regions. Seeker: From this experience it seems that the focus is on other regions"
} | {
"output": "Explainer: So we have an explanation method, called LIME here. We use this explanation method to draw which regions the classification model focuses on when it makes a prediction."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: what do you mean by importance? Seeker: for the different colours Seeker: what is the final convolutional layer Explainer: The red color means the area is of high importance. The blue color means the area is of low importance. The importance means how important the region is when a classification model makes a prediction. Explainer: High important area means the image in these area contains key features for the classification model to classify an image into a category. Explainer: It is a layer in the classification model. The output of that layer is high-dimensional features extracted by the classificaiton model. Explainer: Grad-cam can only be used for deep learning models that have convolutional layers. Seeker: I see Seeker: what is a gradcam Explainer: Grad-CAM is a technique to generate explanations by highlighting regions of an image that are most important for a deep neural network's prediction. Explainer: Here grad-cam model generates the heatmap to explain the prediction from a classification model. Seeker: So the Grad-cam is used to explain the classfication network? Explainer: Yes. exactly. Seeker: what is a heatmap Seeker: What is a deep learning model? any examples? Explainer: The heatmap is the red-blue graph on your left screen. It is used to highlight the regions of the image that are most important for the classification model's prediction. Explainer: Bluer colors are used to represent areas of low importance, while redder colors indicate areas of high importance. Seeker: ok"
} | {
"output": "Explainer: A deep learning model is a type of artificial intelligence that learns from data to make predictions or decisions, like, the classification model can classify an image into a category, the face recognition model can recognize different people's faces, and the language model can answer your questions. Do you have other questions?"
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: with small parts of the picture only seen by AI, how would you ensure that the small parts would make up the desired picture Explainer: The classification model will see the entire image to make a prediction. Seeker: for example, if AI only takes in the leopard spots on certain area, it could be from a picture of a lady wearing leopard spots Explainer: The small parts highlighted by the explanation model show the regions that are most important for the classification to make a prediction. Seeker: so we show a picture of a leopard and the output by AI will state that it is a leopard. what if the input is someone wearing full leopard spots clothing Seeker: based on the model prediction only taking small parts of the input picture. it could possibly only take in the leopard spots Explainer: This will be another task. The output of someone wearing full leopard spots clothing will depends on the label we tell the model. If we tells the model this is a lady and feed the model with many examples, the model will output lady category. Seeker: I see. so basically AI machine learning through multiple inputs like those captcha Explainer: Here the LIME wants to explain if the classification model classifies this image into a leopard, which regions of the input are most important for the model to make such a prediction. Explainer: Yes. The AI model will learn from data. Explainer: We don't know what they learned and what they focus on to make a decision. Seeker: alright. that's understandable. But large samples are required. What about the recent issue about AI thinking that humans eat spaghetti with hands? like those AI generated photos Explainer: The LIME method aims to help humans better understand the behavior of AI models. Seeker: I see. Explainer: Maybe the AI model sees someone eating spaghetti with hands when the model is trained. Explainer: But I'm not sure about this. Seeker: I think it is more of babies eating spaghetti with hands which made AI have the connection of humans eating spaghetti with hands. Would you say that this is a failure in the AI machine learning? or would you say that more models are required Explainer: The AI model is designed to think about the world in a statistical way . Seeker: uh huh Explainer: I cannot say it is a failure. But there would have a better way to design the model in the future. Seeker: With better prediction I guess. There's also an issue where AI has issues with skin tones Seeker: like back then there was this issue with apple phones where the face prediction does not acknowledge people with darker skin? Explainer: Yes. it happens Explainer: It will have differents problems when facing the complex world. Seeker: and would you say that more models will be able to help with this in the future?"
} | {
"output": "Explainer: In my view, it will be."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: What about ai using image based recognition by utilising through experience Explainer: Sorry, I don't understand your question. Seeker: Or through training sets and test sets to determine the reliability of the ai Explainer: The classification model is evaluated by the test set. Explainer: But evaluating a classification model on test sets can only let humans know how accurate the model is. Seeker: Because now from the explanation, i can see that the ai uses heatmap based the image to determine if it matches. Is there any other way to match the image other than the current method observed? Explainer: The heatmap is used to display which region of the input image the classification model focuses on to make a prediction. Seeker: Okay.. just curious but to what extend does it predict the animal reliably? Does it only predict the animals or the species of animal too? Explainer: It depends on the dataset used to train the classification model. Here the classification model can predict different species of animals. Explainer: it can also predict no-living objects, e.g., baby chairs. Seeker: I have seen some stuff where they said they are able to utilise eigen vectors to train or use the ai to recognise the human's face. In this case, it would be different? Explainer: In that task, we need different models and different datasets to do it. Seeker: So as long as the data set covered the relevant objects, as long as the heatmap covers the image that you would want to recognise. Then it will most probably be able to do so?"
} | {
"output": "Explainer: But we can still use the explanation model to explain how those models get their predictions."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: what is swin transformer? Explainer: It is the classification model we used to classify an image into a category. It performs well on image-related tasks. Explainer: For the example on the left screen, the classification model, swin transformer, classifies the first image as the goldfish. Seeker: oh i see. other than that, i could understand the explanation regarding the heatmap etc Seeker: mm how does the grad cam know how much of the color gradient to highlight? eg. some parts below for the 2nd fish is not highlighted as much as the top fish Explainer: The first image is the input of the classification model. The classification mode classifies it as a goldfish. Explainer: But the classification model is complex. We don't understand it. We don't know how it predicts the image as a goldfish. Explainer: And the explanation model generates the second image, which tries to explain which region the classification model focues on to make such decision Explainer: The reason that the classification model focuses on the top fish may be that the top fish is larger and positioned clearly. This makes the classification model easier to identify it. Seeker: \"This heatmap is generated by weighting the activations of the final convolutional layer by their corresponding gradients and averaging the resulting weights spatially.\" could you simplify this explanation for me? Explainer: sure. Explainer: The classification model will first extract some features from original images. Explainer: we will get a feature map. Then we generate the heatmap by calculating how change in this feature map will affect the classification model's prediction. Seeker: i see.. thanks for explaning!"
} | {
"output": "Explainer: if the prediction is sensitive to some features in the feature map, this means those part of the feature is of high importance. Do you have other questions?"
} |