Papers › Dynamic Loss For Robust Learning
Dynamic Loss For Robust Learning
Shenwang Jiang, Jianan Li, Jizhou Zhang, Ying Wang, Tingfa Xu
Label noise and class imbalance commonly coexist in real-world data. Previous works for robust learning, however, usually address either one type of the data biases and underperform when facing them both. To mitigate this gap, this work presents a novel meta-learning based dynamic loss that automatically adjusts the objective functions with the training process to robustly learn a classifier from long-tailed noisy data. Concretely, our dynamic loss comprises a label corrector and a margin generator, which respectively correct noisy labels and generate additive per-class classification margins by perceiving the underlying data distribution as well as the learning state of the classifier. Equipped with a new hierarchical sampling strategy that enriches a small amount of unbiased metadata with diverse and hard samples, the two components in the dynamic loss are optimized jointly through meta-learning and cultivate the classifier to well adapt to clean and balanced test data. Extensive experiments show our method achieves state-of-the-art accuracy on multiple real-world and synthetic datasets with various types of data biases, including CIFAR-10/100, Animal-10N, ImageNet-LT, and Webvision. Code will soon be publicly available.
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Code
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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 | Dynamic Loss (Inception-ResNet-v2) | ImageNet Top-1 Accuracy | 74.76 | #9 of 47 | Archive leaderboard | report |
| Image Classification | mini WebVision 1.0 | Dynamic Loss (Inception-ResNet-v2) | ImageNet Top-5 Accuracy | 93.08 | #9 of 47 | Archive leaderboard | report |
| Image Classification | mini WebVision 1.0 | Dynamic Loss (Inception-ResNet-v2) | Top-1 Accuracy | 80.12 | #9 of 47 | Archive leaderboard | report |
| Image Classification | mini WebVision 1.0 | Dynamic Loss (Inception-ResNet-v2) | Top-5 Accuracy | 93.64 | #9 of 47 | Archive leaderboard | report |
| Learning with noisy labels | ANIMAL | Dynamic Loss | Accuracy | 86.5 | #8 of 19 | Archive leaderboard | report |
| Learning with noisy labels | ANIMAL | Dynamic Loss | ImageNet Pretrained | NO | #8 of 19 | Archive leaderboard | report |
| Learning with noisy labels | ANIMAL | Dynamic Loss | Network | Vgg19-BN | #8 of 19 | 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.
Methods
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