Real-world data universally confronts a severe class-imbalance problem and
exhibits a long-tailed distribution, i.e., most labels are associated with
limited instances. The na\"ive models supervised by such datasets would prefer
dominant labels, encounter a serious generalization challenge and become poorly
calibrated. We propose two novel methods from the prior perspective to
alleviate this dilemma. First, we deduce a balance-oriented data augmentation
named Uniform Mixup (UniMix) to promote mixup in long-tailed scenarios, which
adopts advanced mixing factor and sampler in favor of the minority. Second,
motivated by the Bayesian theory, we figure out the Bayes Bias (Bayias), an
inherent bias caused by the inconsistency of prior, and compensate it as a
modification on standard cross-entropy loss. We further prove that both the
proposed methods ensure the classification calibration theoretically and
empirically. Extensive experiments verify that our strategies contribute to a
better-calibrated model, and their combination achieves state-of-the-art
performance on CIFAR-LT, ImageNet-LT, and iNaturalist 2018.

提出了两种新方法，一种是平衡取向的数据增强（Uniform Mixup），另一种是基于贝叶斯理论的 Bayes Bias，并通过理论和实验证明，这两种方法能够确保分类校准和提高性能。