Papers › Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment
Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment
Mengke Li, Yiu-ming Cheung, Yang Lu
Long-tailed data is still a big challenge for deep neural networks, even though they have achieved great success on balanced data. We observe that vanilla training on long-tailed data with cross-entropy loss makes the instance-rich head classes severely squeeze the spatial distribution of the tail classes, which leads to difficulty in classifying tail class samples. Furthermore, the original cross-entropy loss can only propagate gradient short-lively because the gradient in softmax form rapidly approaches zero as the logit difference increases. This phenomenon is called softmax saturation. It is unfavorable for training on balanced data, but can be utilized to adjust the validity of the samples in long-tailed data, thereby solving the distorted embedding space of long-tailed problems. To this end, this paper proposes the Gaussian clouded logit adjustment by Gaussian perturbation of different class logits with varied amplitude. We define the amplitude of perturbation as cloud size and set relatively large cloud sizes to tail classes. The large cloud size can reduce the softmax saturation and thereby making tail class samples more active as well as enlarging the embedding space. To alleviate the bias in a classifier, we therefore propose the class-based effective number sampling strategy with classifier re-training. Extensive experiments on benchmark datasets validate the superior performance of the proposed method. Source code is available at https://github.com/Keke921/GCLLoss.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Long-tail Learning | CIFAR-10-LT (ρ=10) | GCLLoss | Error Rate | 10.77 | #30 of 50 | Archive leaderboard | report |
| Long-tail Learning | CIFAR-10-LT (ρ=100) | GCL | Error Rate | 17.32 | #15 of 28 | Archive leaderboard | report |
| Long-tail Learning | CIFAR-100-LT (ρ=100) | GCL | Error Rate | 51.29 | #33 of 66 | Archive leaderboard | report |
| Long-tail Learning | CIFAR-100-LT (ρ=50) | GCL | Error Rate | 46.4 | #19 of 25 | 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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