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arXiv · 2006.07737

Generalization by Recognizing Confusion

Daniel Chiu·Franklyn Wang·Scott Duke Kominers
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Abstract

A recently-proposed technique called self-adaptive training augments modern neural networks by allowing them to adjust training labels on the fly, to avoid overfitting to samples that may be mislabeled or otherwise non-representative. By combining the self-adaptive objective with mixup, we further improve the accuracy of self-adaptive models for image recognition; the resulting classifier obtains state-of-the-art accuracies on datasets corrupted with label noise. Robustness to label noise implies a lower generalization gap; thus, our approach also leads to improved generalizability. We find evidence that the Rademacher complexity of these algorithms is low, suggesting a new path towards provable generalization for this type of deep learning model. Last, we highlight a novel connection between difficulties accounting for rare classes and robustness under noise, as rare classes are in a sense indistinguishable from label noise. Our code can be found at https://github.com/Tuxianeer/generalizationconfusion.

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Daniel Chiu, Franklyn Wang, Scott Duke Kominers. 2020-06-13. Generalization by Recognizing Confusion. https://arxiv.org/abs/2006.07737

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