Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels

Erik Englesson; Hossein Azizpour

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arXiv · 2105.04522

Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels

Erik Englesson·Hossein Azizpour

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Abstract

Prior works have found it beneficial to combine provably noise-robust loss functions e.g., mean absolute error (MAE) with standard categorical loss function e.g. cross entropy (CE) to improve their learnability. Here, we propose to use Jensen-Shannon divergence as a noise-robust loss function and show that it interestingly interpolate between CE and MAE with a controllable mixing parameter. Furthermore, we make a crucial observation that CE exhibit lower consistency around noisy data points. Based on this observation, we adopt a generalized version of the Jensen-Shannon divergence for multiple distributions to encourage consistency around data points. Using this loss function, we show state-of-the-art results on both synthetic (CIFAR), and real-world (e.g., WebVision) noise with varying noise rates.

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Erik Englesson, Hossein Azizpour. 2021-05-10. Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels. https://arxiv.org/abs/2105.04522

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