Balanced Contrastive Learning for Long-Tailed Visual Recognition. (arXiv:2207.09052v2 [cs.CV] UPDATED)

Real-world data typically follow a long-tailed distribution, where a few
majority categories occupy most of the data while most minority categories
contain a limited number of samples. Classification models minimizing
cross-entropy struggle to represent and classify the tail classes. Although the
problem of learning unbiased classifiers has been well studied, methods for
representing imbalanced data are under-explored. In this paper, we focus on
representation learning for imbalanced data. Recently, supervised contrastive
learning has shown promising performance on balanced data recently. …

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