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--- |
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annotations_creators: |
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- expert-generated |
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- machine-generated |
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language_creators: |
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- machine-generated |
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language: |
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- en |
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license: |
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- mit |
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multilinguality: |
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- monolingual |
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pretty_name: fashion-mnist-corrupted |
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size_categories: |
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- 10K<n<100K |
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source_datasets: |
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- extended|fashion_mnist |
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task_categories: |
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- image-classification |
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task_ids: [] |
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--- |
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# Fashion-Mnist-C (Corrupted Fashion-Mnist) |
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A corrupted Fashion-MNIST benchmark for testing out-of-distribution robustness of computer vision models, which were trained on Fashion-Mmnist. |
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[Fashion-Mnist](https://github.com/zalandoresearch/fashion-mnist) is a drop-in replacement for MNIST and Fashion-Mnist-C is a corresponding drop-in replacement for [MNIST-C](https://arxiv.org/abs/1906.02337). |
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## Corruptions |
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The following corruptions are applied to the images, equivalently to MNIST-C: |
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- **Noise** (shot noise and impulse noise) |
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- **Blur** (glass and motion blur) |
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- **Transformations** (shear, scale, rotate, brightness, contrast, saturate, inverse) |
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In addition, we apply various **image flippings and turnings**: For fashion images, flipping the image does not change its label, |
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and still keeps it a valid image. However, we noticed that in the nominal fmnist dataset, most images are identically oriented |
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(e.g. most shoes point to the left side). Thus, flipped images provide valid OOD inputs. |
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Most corruptions are applied at a randomly selected level of *severity*, s.t. some corrupted images are really hard to classify whereas for others the corruption, while present, is subtle. |
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## Examples |
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| Turned | Blurred | Rotated | Noise | Noise | Turned | |
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| ------------- | ------------- | --------| --------- | -------- | --------- | |
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| <img src="https://github.com/testingautomated-usi/fashion-mnist-c/raw/main/generated/png-examples/single_0.png" width="100" height="100"> | <img src="https://github.com/testingautomated-usi/fashion-mnist-c/raw/main/generated/png-examples/single_1.png" width="100" height="100"> | <img src="https://github.com/testingautomated-usi/fashion-mnist-c/raw/main/generated/png-examples/single_6.png" width="100" height="100"> | <img src="https://github.com/testingautomated-usi/fashion-mnist-c/raw/main/generated/png-examples/single_3.png" width="100" height="100"> | <img src="https://github.com/testingautomated-usi/fashion-mnist-c/raw/main/generated/png-examples/single_4.png" width="100" height="100"> | <img src="https://github.com/testingautomated-usi/fashion-mnist-c/raw/main/generated/png-examples/single_5.png" width="100" height="100"> | |
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## Citation |
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If you use this dataset, please cite the following paper: |
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``` |
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@inproceedings{Weiss2022SimpleTechniques, |
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title={Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning}, |
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author={Weiss, Michael and Tonella, Paolo}, |
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booktitle={Proceedings of the 31th ACM SIGSOFT International Symposium on Software Testing and Analysis}, |
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year={2022} |
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} |
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``` |
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Also, you may want to cite FMNIST and MNIST-C. |
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## Credits |
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- Fashion-Mnist-C is inspired by Googles MNIST-C and our repository is essentially a clone of theirs. See their [paper](https://arxiv.org/abs/1906.02337) and [repo](https://github.com/google-research/mnist-c). |
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- Find the nominal (i.e., non-corrupted) Fashion-MNIST dataset [here](https://github.com/zalandoresearch/fashion-mnist). |
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