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arXiv · 2105.00127

Breadcrumbs: Adversarial Class-Balanced Sampling for Long-tailed Recognition

Bo Liu·Haoxiang Li·Hao Kang·Gang Hua·Nuno Vasconcelos
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Abstract

The problem of long-tailed recognition, where the number of examples per class is highly unbalanced, is considered. While training with class-balanced sampling has been shown effective for this problem, it is known to over-fit to few-shot classes. It is hypothesized that this is due to the repeated sampling of examples and can be addressed by feature space augmentation. A new feature augmentation strategy, EMANATE, based on back-tracking of features across epochs during training, is proposed. It is shown that, unlike class-balanced sampling, this is an adversarial augmentation strategy. A new sampling procedure, Breadcrumb, is then introduced to implement adversarial class-balanced sampling without extra computation. Experiments on three popular long-tailed recognition datasets show that Breadcrumb training produces classifiers that outperform existing solutions to the problem.

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Bo Liu, Haoxiang Li, Hao Kang, Gang Hua, Nuno Vasconcelos. 2021-05-01. Breadcrumbs: Adversarial Class-Balanced Sampling for Long-tailed Recognition. https://arxiv.org/abs/2105.00127

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