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• W3163351377 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 (WebVision) noise with varying noise rates." @default.
• W3163351377 created "2021-05-24" @default.
• W3163351377 creator A5050426259 @default.
• W3163351377 creator A5071284506 @default.
• W3163351377 date "2021-05-10" @default.
• W3163351377 modified "2023-10-01" @default.
• W3163351377 title "Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels" @default.
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• W3163351377 hasPublicationYear "2021" @default.
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