MNIST-C: A Robustness Benchmark for Computer Vision MNIST-C: A Robustness Benchmark for Computer Vision
Paper summary Mu and Gilmer introduce MNIST-C, an MNIST-based corruption benchmark for out-of-distribution evaluation. The benchmark includes various corruption types including random noise (shot and impulse noise), blur (glass and motion blur), (affine) transformations, “striping” or occluding parts of the image, using Canny images or simulating fog. These corruptions are also shown in Figure 1. The transformations have been chosen to be semantically invariant, meaning that the true class of the image does not change. This is important for evaluation as model’s can easily be tested whether they still predict the correct labels on the corrupted images. https://i.imgur.com/Y6LgAM4.jpg Figure 1: Examples of the used corruption types included in MNIST-C. Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).
arxiv.org
scholar.google.com
MNIST-C: A Robustness Benchmark for Computer Vision
Mu, Norman and Gilmer, Justin
arXiv e-Print archive - 2019 via Local Bibsonomy
Keywords: dblp


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Summary by David Stutz 8 months ago
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