Using learned optimizers to make models robust to input noise

CoRR, 2019.

Cited by: 6|Bibtex|Views74
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Other Links: dblp.uni-trier.de|arxiv.org

Abstract:

State-of-the art vision models can achieve superhuman performance on image classification tasks when testing and training data come from the same distribution. However, when models are tested on corrupted images (e.g. due to scale changes, translations, or shifts in brightness or contrast), performance degrades significantly. Here, we e...More

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