Papers › Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization
Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization
Kaidi Cao, Yining Chen, Junwei Lu, Nikos Arechiga, Adrien Gaidon, Tengyu Ma
Real-world large-scale datasets are heteroskedastic and imbalanced -- labels have varying levels of uncertainty and label distributions are long-tailed. Heteroskedasticity and imbalance challenge deep learning algorithms due to the difficulty of distinguishing among mislabeled, ambiguous, and rare examples. Addressing heteroskedasticity and imbalance simultaneously is under-explored. We propose a data-dependent regularization technique for heteroskedastic datasets that regularizes different regions of the input space differently. Inspired by the theoretical derivation of the optimal regularization strength in a one-dimensional nonparametric classification setting, our approach adaptively regularizes the data points in higher-uncertainty, lower-density regions more heavily. We test our method on several benchmark tasks, including a real-world heteroskedastic and imbalanced dataset, WebVision. Our experiments corroborate our theory and demonstrate a significant improvement over other methods in noise-robust deep learning.
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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 | WebVision-1000 | HAR (InceptionResNet-v2) | ImageNet Top-1 Accuracy | 67.1% | #11 of 16 | Archive leaderboard | report |
| Image Classification | WebVision-1000 | HAR (InceptionResNet-v2) | ImageNet Top-5 Accuracy | 86.7% | #11 of 16 | Archive leaderboard | report |
| Image Classification | WebVision-1000 | HAR (InceptionResNet-v2) | Top-1 Accuracy | 75.0% | #11 of 16 | Archive leaderboard | report |
| Image Classification | WebVision-1000 | HAR (InceptionResNet-v2) | Top-5 Accuracy | 90.6% | #11 of 16 | 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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