Class Confidence Aware Reweighting for Long Tailed Learning

TL;DR

This paper introduces a novel loss-level re-weighting scheme that dynamically adjusts training contributions based on class confidence and frequency, improving long-tailed learning performance.

Contribution

It proposes a confidence-aware re-weighting method that complements existing logit adjustments, enhancing long-tailed class imbalance handling at the loss level.

Findings

Significant improvements on CIFAR-100-LT, ImageNet-LT, and iNaturalist2018 datasets.

Effective handling of class imbalance across various imbalance factors.

Theoretical validation of the proposed re-weighting scheme.

Abstract

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 {\Omega}(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.