Mutual Learning for Long-Tailed Recognition

Changhwa Park, Junho Yim, Eunji Jun

Abstract: Deep neural networks perform well in artificially-balanced datasets, but real-world data often has a long-tailed distribution. Recent studies have focused on developing unbiased classifiers to improve tail class performance. Despite the efforts to learn a fine classifier, we cannot guarantee a solid performance if the representations are of poor quality. However, learning high-quality representations in a long-tailed setting is difficult because the features of tail classes easily overfit the training dataset. In this work, we propose a mutual learning framework that generates high-quality representations in long-tailed settings by exchanging information between networks. We show that the proposed method can improve representation quality and establish a new state-of-the-art record on several long-tailed recognition benchmark datasets, including CIFAR100-LT, ImageNet-LT, and iNaturalist 2018.