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 [
     [
         {
-            "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? Explainer: The heatmap is generated based on the importance of image regions to the classification model. Explainer: 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. Explainer: 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. Seeker: I see. So a heat sensor is not required? A heatmap is automatically generated from each photo and analysed using the model."
+            "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 Explainer: The classification models are fundamental models for many other tasks, like face recognition or self-driving. Explainer: For the explanation method, it can be used in many sensitive areas, like healthcare, finance, and legal systems Explainer: We can use the explanation method to understand the reasons behind a machine learning model's prediction. Seeker: ok cool Seeker: the model is to identify the object, can it identify the number of objects ? Explainer: It can do that. But here, it was trained to recognize the main object in the image. Seeker: ok Seeker: if the fish in the image is very small compared to the one here, can it still predict accurately Seeker: does the size matter"
         },
         {
-            "output": "Explainer: Yes. exactly. There is no heat sensor. The second image is just shown in a heatmap way to illustrate which regions are important to the classification model."
+            "output": "Explainer: I'm not sure about this. It will depend on the data we use to train the classification model. But usually, size matters. As large size of the objects may contain more features that can be detected by models."
         }
     ],
     [
         {
-            "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?"
+            "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?"
         },
         {
-            "output": "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."
+            "output": "Explainer: especially in some domains, such as healthcare, finance, and legal systems, where understanding the reasons behind a deep learning model\u2019s prediction can impact critical decision-making processes. Yes. The LIME method can be used to explain image-input and text-input models"
+        }
+    ],
+    [
+        {
+            "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?"
+        },
+        {
+            "output": "Explainer: The LIME model is used to explain the classification model. The classification model is a deep learning model, which is very complex to understand for humans. Explanation models, like LIME we used here, are used to explain how the classification model makes such a prediction. 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."
+        }
+    ],
+    [
+        {
+            "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"
+        },
+        {
+            "output": "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. The swin transformer here can identify different breeds of birds, fish, or other animals."
+        }
+    ],
+    [
+        {
+            "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?"
+        },
+        {
+            "output": "Explainer: Sorry, what do you mean by retroactively? Grad-cam will generate explanations after a learning model makes 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 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?"
+        },
+        {
+            "output": "Explainer: Not exactly. the purpose of the perturbations is not really to train the classification model, but to understand it better. 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. 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."
+        }
+    ],
+    [
+        {
+            "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"
+        },
+        {
+            "output": "Explainer: Yes. We can do it with corresponding data."
+        }
+    ],
+    [
+        {
+            "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? Explainer: But we can still use the explanation model to explain how those models get their predictions. Seeker: Oh okay Explainer: as long as the data set covered the relevant objects, we can design a model to do it. Explainer: The heatmap will not be used to train the model. It will explain how the model made decisions after the model is trained. Seeker: Uhm based on this current ai, what is the reliability? Or how accurate it is currently Explainer: For the classification task on the left screen, the best model can reach 84% accuracy. Seeker: Back to the heatmap question, then how would the ai be able to correctly identify it? Based on test and train sets? Where you would correct the ai if it is wrong? Explainer: For the heatmap, it is generated by an explanation method, called grad-cam. Explainer: It will calculate how changes to each region of the image would affect the classification model's output. Seeker: Oh, so for every test sets, you would highlight the importances of significance for each photo?"
+        },
+        {
+            "output": "Explainer: Yes."
+        }
+    ],
+    [
+        {
+            "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"
+        },
+        {
+            "output": "Explainer: The AI model is designed to think about the world in a statistical way ."
+        }
+    ],
+    [
+        {
+            "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"
+        },
+        {
+            "output": "Explainer: A good classification model should assign high probability to both shark and goldfish if an image contains both of them. 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. it's the same for the goldfish."
+        }
+    ],
+    [
+        {
+            "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? Explainer: I'm not sure about the exact number of convolutional layers in the swin transformer model. Explainer: A convolutional layer contains several kernels to extract features from the input. Seeker: I see. Thus the final layer consolidates the most important features identified by the method."
+        },
+        {
+            "output": "Explainer: Yes. exactly. 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 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?"
+        },
+        {
+            "output": "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."
+        }
+    ],
+    [
+        {
+            "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"
+        },
+        {
+            "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."
+        }
+    ],
+    [
+        {
+            "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?"
+        },
+        {
+            "output": "Explainer: The classification model will learn to give a prediction of categories based on the input image."
+        }
+    ],
+    [
+        {
+            "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"
+        },
+        {
+            "output": "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. High important area means the image in these area contains key features for the classification model to classify an image into a category. It is a layer in the classification model. The output of that layer is high-dimensional features extracted by the classificaiton model. Grad-cam can only be used for deep learning models that have convolutional layers."
+        }
+    ],
+    [
+        {
+            "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?"
+        },
+        {
+            "output": "Explainer: We can sample more data points around the input data and carefully choose the surrogate models. 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 the model prediction only part of the leopard's face?"
+        },
+        {
+            "output": "Explainer: The classification model's prediction is the image contains a leopard."
+        }
+    ],
+    [
+        {
+            "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 Explainer: Sure. I can explain it to you. Explainer: the final convolutional layer of the classification model will generate feature maps of the original image. Explainer: Grad-CAM  will calculate the gradients of the output class with respect to each features in the feature maps Explainer: The gradients are how changes to each feature would affect the network's output. Seeker: ok, got it! Explainer: The more sensitive the network output is to the feature, the higher gradients we will get for that feature. Explainer: Am i clear about this? Seeker: yep it is understandable for laypeople Explainer: Thanks. Explainer: Do you have other quesitons? 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: 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? Explainer: I'm not sure about the exact number of convolutional layers in the swin transformer model. Explainer: A convolutional layer contains several kernels to extract features from the input. Seeker: I see. Thus the final layer consolidates the most important features identified by the method. Explainer: Yes. exactly. Explainer: Do you have other questions? Seeker: No, that's all."
+        },
+        {
+            "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: 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?"
+        },
+        {
+            "output": "Explainer: The model will have a high probability to classify the image into the dog category or the goldfish category When we observe how the image is classified into the dog category, a good classification model should focus on the area of dogs. 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."
+        }
+    ],
+    [
+        {
+            "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 Explainer: The classification models are fundamental models for many other tasks, like face recognition or self-driving. Explainer: For the explanation method, it can be used in many sensitive areas, like healthcare, finance, and legal systems Explainer: We can use the explanation method to understand the reasons behind a machine learning model's prediction. Seeker: ok cool Seeker: the model is to identify the object, can it identify the number of objects ?"
+        },
+        {
+            "output": "Explainer: It can do that. But here, it was trained to recognize the main object in the 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 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"
+        },
+        {
+            "output": "Explainer: The classification model will have a high probability to classify the image into all animals. When we observe how the image is classified into one animal, a good model should focus on the area of that animal. 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."
+        }
+    ],
+    [
+        {
+            "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?"
+        },
+        {
+            "output": "Explainer: Yes. There are many other explanation methods, like LIME, or integrated gradients, and also many studies to compare them."
+        }
+    ],
+    [
+        {
+            "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?"
+        },
+        {
+            "output": "Explainer: Therefore we need the explanation method, like Grad-cam here to understand how the black box model makes a prediction. It cannot be applied to all types of deep learning models. It can only be used for deep learning models that have convolutional layers."
+        }
+    ],
+    [
+        {
+            "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"
+        },
+        {
+            "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 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?"
+        },
+        {
+            "output": "Explainer: The classification model here can reach 84% accuracy. 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."
+        }
+    ],
+    [
+        {
+            "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"
+        },
+        {
+            "output": "Explainer: 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. And the explanation model generates the second image, which tries to explain which region the classification model focues on to make such decision 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."
+        }
+    ],
+    [
+        {
+            "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"
+        },
+        {
+            "output": "Explainer: It depends. If objects' unique visual characters are not hidden by others, the classification model may identify them. 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."
+        }
+    ],
+    [
+        {
+            "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\u2019s 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"
+        },
+        {
+            "output": "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. 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 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"
+        },
+        {
+            "output": "Explainer: We measure the classification model by the accuracy of predicting correct labels."
+        }
+    ],
+    [
+        {
+            "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?"
+        },
+        {
+            "output": "Explainer: Yes. There are many models that can be used to classify images."
+        }
+    ],
+    [
+        {
+            "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"
+        },
+        {
+            "output": "Explainer: Yes, exactly. insufficient data can lead to false predictions."
+        }
+    ],
+    [
+        {
+            "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 Explainer: The classification models are fundamental models for many other tasks, like face recognition or self-driving. Explainer: For the explanation method, it can be used in many sensitive areas, like healthcare, finance, and legal systems Explainer: We can use the explanation method to understand the reasons behind a machine learning model's prediction. Seeker: ok cool Seeker: the model is to identify the object, can it identify the number of objects ? Explainer: It can do that. But here, it was trained to recognize the main object in the image. Seeker: ok Seeker: if the fish in the image is very small compared to the one here, can it still predict accurately Seeker: does the size matter Explainer: I'm not sure about this. It will depend on the data we use to train the classification model. Explainer: But usually, size matters. As large size of the objects may contain more features that can be detected by models. Seeker: ok! Seeker: im like running out of 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: 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\u2019s 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"
+        },
+        {
+            "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 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 Explainer: Can you specify more about this question? Seeker: has it been used as a tool in the public Seeker: non-research Explainer: The LIME method will be used to explain the predictions of deep learning models. So we can use it to observe the behavior of deep learning models. Explainer: It can be used in some complex domains where interpretability is crucial, such as healthcare, finance, and legal systems. Explainer: But I'm not sure how many practical applications use it. 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 swim transformer"
+        },
+        {
+            "output": "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. Here we use the swin transformer to classify an image into a category."
+        }
+    ],
+    [
+        {
+            "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"
+        },
+        {
+            "output": "Explainer: Can you ask more questions?"
+        }
+    ],
+    [
+        {
+            "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?"
+        },
+        {
+            "output": "Explainer: LIME will segments the image into a set of superpixels (i.e., contiguous pixels that are similar to each other) Then it will find the top 5 super-pixels that are important to the model 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"
+        },
+        {
+            "output": "Explainer: It is a deep learning model that can classify an image into a category For the example on the left screen, the classification model classifies the first image as goldfish."
+        }
+    ],
+    [
+        {
+            "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 ?"
+        },
+        {
+            "output": "Explainer: Yes. Can you ask more 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? 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? Explainer: The classification model will be tested on a dataset. We choose the model based on accuracy. Seeker: Ok. Will there be cases where the model makes an incorrect prediction? How will that be explained? Explainer: Yes. the classification model is not 100% accurate. It will make wrong predictions. Explainer: We can use the explanation method to find out why the model makes a wrong prediction. Explainer: For the user, we can use the explanation to decide whether we an believe the prediction or not. Explainer: If the explanation is not reasonable, we can reject the prediction. Explainer: For researchers, the explanation of the wrong results can help them find which part of the input leads the model to predict wrong. The researcher can adjust or improve the model based on that. Seeker: What kind of improvements can be made to make the model more accurate in its predictions?"
+        },
+        {
+            "output": "Explainer: As the classification model learn how to make predictions from a large set of data, we can adjust the dataset by introducing more images that the model performs bad no. We can also modify the structure of the model to make the model perform better."
+        }
+    ],
+    [
+        {
+            "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"
+        },
+        {
+            "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: 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 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 Seeker: So how much weight does the model need to classify an image with a high degree of accuracy?"
+        },
+        {
+            "output": "Explainer: it highly depends on the model itself, the complexity of the image, and the nature of the object being classified. Some models might require only a few distinctive features, while others may rely on more. So I cannot provide you with a precise number."
+        }
+    ],
+    [
+        {
+            "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 Explainer: In these three options, we have three classification models with different accuracy. Explainer: We use an explanation model to explain how they make a prediction. Explainer: 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. Explainer: A good model also should focus on more unique visual characters as possible. Explainer: And we want you to choose among three options which one is the best classification model based on the explanation of their prediction. Seeker: oh okok Seeker: if u fed more than 1000 images, would the system be more accurate? Seeker: can they start to detect specific people for example"
+        },
+        {
+            "output": "Explainer: If we have many images for specific people, the model may be able to do it. Possible. But I'm not sure about this. 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 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?"
+        },
+        {
+            "output": "Explainer: It's not the best model. There are classification models performing better than swin transformer. But it performs better than AlexNet or VGG16. But swin transformer can reach 84% accuracy on a very large dataset. Do you have any questions about the explanation method, LIME?"
+        }
+    ],
+    [
+        {
+            "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 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 Seeker: So how much weight does the model need to classify an image with a high degree of accuracy? Explainer: it highly depends on the model itself, the complexity of the image, and the nature of the object being classified. Some models might require only a few distinctive features, while others may rely on more. So I cannot provide you with a precise number. Seeker: I see, is there any reason why LIME was selected as an explanation model then? Explainer: It is because the LIME method can be applied to any type of deep learning model. Explainer: Some other methods are model-specific, which means they can only be applied to a certain type of deep learning model. Seeker: I think that is probably it, I really can't think of any other queries. \nBut I must say the conversation helped"
+        },
+        {
+            "output": "Explainer: Thanks a lot 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\""
+        },
+        {
+            "output": "Explainer: The classification model will contain many layers to extract features from the image. the convolutional layer is one of them."
+        }
+    ],
+    [
+        {
+            "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"
+        },
+        {
+            "output": "Explainer: These are regions of an image that are most important for the classification 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 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? Explainer: The classification model will be tested on a dataset. We choose the model based on accuracy. Seeker: Ok. Will there be cases where the model makes an incorrect prediction? How will that be explained?"
+        },
+        {
+            "output": "Explainer: Yes. the classification model is not 100% accurate. It will make wrong predictions. We can use the explanation method to find out why the model makes a wrong prediction. For the user, we can use the explanation to decide whether we an believe the prediction or not. If the explanation is not reasonable, we can reject the prediction. For researchers, the explanation of the wrong results can help them find which part of the input leads the model to predict wrong. The researcher can adjust or improve the model based on that."
+        }
+    ],
+    [
+        {
+            "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 ?"
+        },
+        {
+            "output": "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 A heatmap is like a colorful overlay on the picture that helps you see what the classification model is focusing on."
         }
     ]
 ]