根据类别频率和预测置信度动态重加权，提升长尾数据下的模型性能。
Class Confidence Aware Reweighting for Long Tailed Learning
- 基于损失层级设计置信度与类别频率感知的重加权机制
- 在CIFAR-100-LT等数据集上显著提升尾部类别准确率
- 适用于长尾分布场景，尤其适合对小样本类别敏感的任务
深度神经网络在长尾数据分布下性能明显下降，因头部类别样本过多而尾部类别样本过少。现有方法多关注决策空间中的修正，如对数偏移以补偿类别先验偏差，却较少关注优化过程中样本置信度差异带来的影响。本文提出一种纯基于损失层级的类别与置信度感知重加权方案，通过Ω(p_t, f_c)函数调节训练贡献，结合预测置信度与类别相对频率。实验在CIFAR-100-LT、ImageNet-LT和iNaturalist2018上，于多种失衡因子条件下均取得显著效果，验证了理论分析的有效性。
原文摘要 · Abstract (English)
Deep neural network models degrade significantly in the long-tailed data distribution, with the overall training data dominated by a small set of classes in the head, and the tail classes obtaining less training examples. Addressing the imbalance in the classes, attention in the related literature was given mainly to the adjustments carried out in the decision space in terms of either corrections performed at the logit level in order to compensate class-prior bias, with the least attention to the optimization process resulting from the adjustments introduced through the differences in the confidences among the samples. In the current study, we present the design of a class and confidence-aware re-weighting scheme for long-tailed learning. This scheme is purely based upon the loss level and has a complementary nature to the existing methods performing the adjustment of the logits. In the practical implementation stage of the proposed scheme, we use an Ω(p_t, f_c) function. This function enables the modulation of the contribution towards the training task based upon the confidence value of the prediction, as well as the relative frequency of the corresponding class. Our observations in the experiments are corroborated by significant experimental results performed on the CIFAR-100-LT, ImageNet-LT, and iNaturalist2018 datasets under various values of imbalance factors that clearly authenticate the theoretical discussions above.
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