Papers › Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels
Erik Englesson, Hossein Azizpour
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | mini WebVision 1.0 | GJS (ResNet-50) | ImageNet Top-1 Accuracy | 75.50 | #18 of 47 | Archive leaderboard | report |
| Image Classification | mini WebVision 1.0 | GJS (ResNet-50) | ImageNet Top-5 Accuracy | 91.27 | #18 of 47 | Archive leaderboard | report |
| Image Classification | mini WebVision 1.0 | GJS (ResNet-50) | Top-1 Accuracy | 79.28 | #18 of 47 | Archive leaderboard | report |
| Image Classification | mini WebVision 1.0 | GJS (ResNet-50) | Top-5 Accuracy | 91.22 | #18 of 47 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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