Papers › Feature Space Augmentation for Long-Tailed Data
Feature Space Augmentation for Long-Tailed Data
Peng Chu, Xiao Bian, Shaopeng Liu, Haibin Ling
Real-world data often follow a long-tailed distribution as the frequency of each class is typically different. For example, a dataset can have a large number of under-represented classes and a few classes with more than sufficient data. However, a model to represent the dataset is usually expected to have reasonably homogeneous performances across classes. Introducing class-balanced loss and advanced methods on data re-sampling and augmentation are among the best practices to alleviate the data imbalance problem. However, the other part of the problem about the under-represented classes will have to rely on additional knowledge to recover the missing information. In this work, we present a novel approach to address the long-tailed problem by augmenting the under-represented classes in the feature space with the features learned from the classes with ample samples. In particular, we decompose the features of each class into a class-generic component and a class-specific component using class activation maps. Novel samples of under-represented classes are then generated on the fly during training stages by fusing the class-specific features from the under-represented classes with the class-generic features from confusing classes. Our results on different datasets such as iNaturalist, ImageNet-LT, Places-LT and a long-tailed version of CIFAR have shown the state of the art performances.
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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 | iNaturalist 2018 | ResNet-152 | Top-1 Accuracy | 69.08% | #40 of 60 | Archive leaderboard | report |
| Image Classification | iNaturalist 2018 | ResNet-101 | Top-1 Accuracy | 68.39% | #43 of 60 | Archive leaderboard | report |
| Image Classification | iNaturalist 2018 | ResNet-50 | Top-1 Accuracy | 65.91% | #47 of 60 | Archive leaderboard | report |
| Long-tail Learning | ImageNet-LT | Online Feature Augmentation | Top-1 Accuracy | 35.3 | #68 of 69 | Archive leaderboard | report |
| Long-tail Learning | Places-LT | Online Feature Augmentation | Top-1 Accuracy | 36.4 | #26 of 29 | Archive leaderboard | report |
| Long-tail Learning | iNaturalist 2018 | Online Feature Augmentation | Top-1 Accuracy | 65.9% | #43 of 43 | 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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