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.

翻译：深度神经网络在训练数据呈长尾分布时，常因多数类主导训练而导致模型偏差，进而出现性能退化。近年研究致力于通过从数据空间获取优质表征来解决该问题，但鲜有研究关注特征范数对预测结果的影响。为此，本文从特征空间出发解决长尾问题，并提出特征平衡损失。具体而言，我们通过给予尾类更强烈的刺激，鼓励其获得更大的特征范数。此外，刺激强度采用课程学习的方式逐步增强，这既提升了尾类的泛化能力，同时保持了头类的性能。在多个主流长尾识别基准上的大量实验表明，与最先进方法相比，特征平衡损失取得了卓越的性能提升。