Long-tailed recognition with imbalanced class distribution naturally emerges in practical machine learning applications. Existing methods such as data reweighing, resampling, and supervised contrastive learning enforce the class balance with a price of introducing imbalance between instances of head class and tail class, which may ignore the underlying rich semantic substructures of the former and exaggerate the biases in the latter. We overcome these drawbacks by a novel ``subclass-balancing contrastive learning (SBCL)'' approach that clusters each head class into multiple subclasses of similar sizes as the tail classes and enforce representations to capture the two-layer class hierarchy between the original classes and their subclasses. Since the clustering is conducted in the representation space and updated during the course of training, the subclass labels preserve the semantic substructures of head classes. Meanwhile, it does not overemphasize tail class samples, so each individual instance contribute to the representation learning equally. Hence, our method achieves both the instance- and subclass-balance, while the original class labels are also learned through contrastive learning among subclasses from different classes. We evaluate SBCL over a list of long-tailed benchmark datasets and it achieves the state-of-the-art performance. In addition, we present extensive analyses and ablation studies of SBCL to verify its advantages.

翻译：长尾识别中因类别分布不平衡而自然出现在实际机器学习应用中。现有方法如数据重加权、重采样及监督对比学习，在实现类别平衡的同时，会引入头部类与尾部类实例间的不平衡，这可能忽略前者丰富的语义子结构，并放大后者的偏差。我们通过一种新颖的"子类平衡对比学习（SBCL）"方法克服这些缺陷，该方法将每个头部类聚合成与尾部类规模相近的多个子类，并强制表征捕捉原始类别与其子类之间的两层类层次结构。由于聚类在表征空间中进行并在训练过程中更新，子类标签保留了头部类的语义子结构。同时，该方法不过度强调尾部类样本，使每个单独实例对表征学习做出同等贡献。因此，我们的方法实现了实例平衡与子类平衡，同时通过不同类别子类间的对比学习学习原始类标签。我们在多个长尾基准数据集上评估SBCL，它达到了最先进的性能。此外，我们提供了SBCL的广泛分析和消融研究以验证其优势。