Learning generalizable representation and classifier for class-imbalanced data is challenging for data-driven deep models. Most studies attempt to re-balance the data distribution, which is prone to overfitting on tail classes and underfitting on head classes. In this work, we propose Dual Compensation Residual Networks to better fit both tail and head classes. Firstly, we propose dual Feature Compensation Module (FCM) and Logit Compensation Module (LCM) to alleviate the overfitting issue. The design of these two modules is based on the observation: an important factor causing overfitting is that there is severe feature drift between training and test data on tail classes. In details, the test features of a tail category tend to drift towards feature cloud of multiple similar head categories. So FCM estimates a multi-mode feature drift direction for each tail category and compensate for it. Furthermore, LCM translates the deterministic feature drift vector estimated by FCM along intra-class variations, so as to cover a larger effective compensation space, thereby better fitting the test features. Secondly, we propose a Residual Balanced Multi-Proxies Classifier (RBMC) to alleviate the under-fitting issue. Motivated by the observation that re-balancing strategy hinders the classifier from learning sufficient head knowledge and eventually causes underfitting, RBMC utilizes uniform learning with a residual path to facilitate classifier learning. Comprehensive experiments on Long-tailed and Class-Incremental benchmarks validate the efficacy of our method.

翻译：学习类别不平衡数据的可泛化表示与分类器对数据驱动的深度模型具有挑战性。大多数研究尝试重新平衡数据分布，但这一方法易导致对尾部类别的过拟合和对头部类别的欠拟合。本文提出双补偿残差网络以更好地拟合尾部与头部类别。首先，我们提出双重特征补偿模块（FCM）与逻辑补偿模块（LCM）以缓解过拟合问题。这两个模块的设计基于以下观察：导致过拟合的重要因素是尾部类别训练数据与测试数据之间存在严重的特征漂移。具体而言，测试阶段尾部类别的特征倾向于向多个相似头部类别的特征云漂移。因此，FCM为每个尾部类别估计多模态特征漂移方向并对其进行补偿。此外，LCM将FCM估计的确定性特征漂移向量沿类内变异方向进行平移，以覆盖更大的有效补偿空间，从而更好地拟合测试特征。其次，我们提出残差平衡多代理分类器（RBMC）以缓解欠拟合问题。受重新平衡策略阻碍分类器学习充分头部知识并最终导致欠拟合这一观察的启发，RBMC利用带有残差路径的均衡学习促进分类器学习。在长尾分布与类增量学习基准上的综合实验验证了本方法的有效性。