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.

翻译：深度神经网络模型在长尾数据分布下性能显著下降，整体训练数据由头部少数类别主导，而尾部类别获得的训练样本较少。针对类别不平衡问题，相关文献的关注点主要集中于决策空间中的调整，即通过对数层面的修正以补偿类别先验偏差，而对由样本间置信度差异引入调整所导致的优化过程关注最少。在本研究中，我们提出了一种用于长尾学习的类别与置信度感知重加权方案。该方案完全基于损失层面设计，并与现有执行对数调整的方法具有互补性。在所提方案的实际实现阶段，我们采用Ω(p_t, f_c)函数。该函数能够根据预测的置信度值以及对应类别的相对频率，调节其对训练任务的贡献。我们在CIFAR-100-LT、ImageNet-LT和iNaturalist2018数据集上，针对不同不平衡因子值进行的实验取得了显著成果，这些观察结果有力地验证了上述理论讨论。