Deep neural networks frequently suffer from performance degradation when the
training data is long-tailed because several majority classes dominate the
training, resulting in a biased model. Recent studies have made a great effort
in solving this issue by obtaining good representations from data space, but
few of them pay attention to the influence of feature norm on the predicted
results. In this paper, we therefore address the long-tailed problem from
feature space and thereby propose the feature-balanced loss. Specifically, we
encourage larger feature norms of tail classes by giving them relatively
stronger stimuli. Moreover, the stimuli intensity is gradually increased in the
way of curriculum learning, which improves the generalization of the tail
classes, meanwhile maintaining the performance of the head classes. Extensive
experiments on multiple popular long-tailed recognition benchmarks demonstrate
that the feature-balanced loss achieves superior performance gains compared
with the state-of-the-art methods.

本文提出了通过使用平衡的特征损失，通过课程学习来加强长尾类别的激励，强化尾部类别的特征规范，从而提高了长尾问题的性能，并在多个受欢迎的长尾识别基准上实现了优越的性能增益。