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--- |
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license: cc-by-sa-3.0 |
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task_categories: |
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- image-classification |
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language: |
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- en |
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pretty_name: mnist_ambigous |
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size_categories: |
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- 10K<n<100K |
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source_datasets: |
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- extended|mnist |
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annotations_creators: |
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- machine-generated |
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--- |
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# Mnist-Ambiguous |
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This dataset contains mnist-like images, but with an unclear ground truth. For each image, there are two classes which could be considered true. |
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Robust and uncertainty-aware DNNs should thus detect and flag these issues. |
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### Features |
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Same as mnist, the supervised dataset has an `image` (28x28 int array) and a `label` (int). |
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Additionally, the following features are exposed for your convenience: |
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- `text_label` (str): A textual representation of the probabilistic label, e.g. `p(0)=0.54, p(5)=0.46` |
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- `p_label` (list of floats): Ground-Truth probabilities for each class (two nonzero values for our ambiguous images) |
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- `is_ambiguous` (bool): Flag indicating if this is one of our ambiguous images (see 'splits' below) |
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### Splits |
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We provide four splits: |
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- `test`: 10'000 ambiguous images |
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- `train`: 10'000 ambiguous images - adding ambiguous images to the training set makes sure test-time ambiguous images are in-distribution. |
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- `test_mixed`: 20'000 images, consisting of the (shuffled) concatenation of our ambiguous `test` set and the nominal mnist test set by LeCun et. al., |
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- `train_mixed`: 70'000 images, consisting of the (shuffled) concatenation of our ambiguous `training` and the nominal training set. |
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Note that the ambiguous test images are highly ambiguous (i.e., the two classes have very similar ground truth likelihoods), |
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the training set images allow for more unbalanced ambiguity. |
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This is to make the training set more closely connected to the nominal data, while still keeping the test set clearly ambiguous. |
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For research targeting explicitly aleatoric uncertainty, we recommend training the model using `train_mixed`. |
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Otherwise, our `test` set will lead to both epistemic and aleatoric uncertainty. |
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In related literature, such 'mixed' splits are sometimes denoted as *dirty* splits. |
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### Assessment and Validity |
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For a brief discussion of the strength and weaknesses of this dataset, |
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including a quantitative comparison to the (only) other ambiguous datasets available in the literature, we refer to our paper. |
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### Paper |
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Pre-print here: [https://arxiv.org/abs/2207.10495](https://arxiv.org/abs/2207.10495) |
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Citation: |
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``` |
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@misc{https://doi.org/10.48550/arxiv.2207.10495, |
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doi = {10.48550/ARXIV.2207.10495}, |
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url = {https://arxiv.org/abs/2207.10495}, |
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author = {Weiss, Michael and Gómez, André García and Tonella, Paolo}, |
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title = {A Forgotten Danger in DNN Supervision Testing: Generating and Detecting True Ambiguity}, |
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publisher = {arXiv}, |
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year = {2022} |
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} |
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``` |
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### License |
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As this is a derivative work of mnist, which is CC-BY-SA 3.0 licensed, our dataset is released using the same license. |
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