Contrastive learning (CL) is a predominant technique in image classification, but they showed limited performance with an imbalanced dataset. Recently, several supervised CL methods have been proposed to promote an ideal regular simplex geometric configuration in the representation space-characterized by intra-class feature collapse and uniform inter-class mean spacing, especially for imbalanced datasets. In particular, existing prototype-based methods include class prototypes, as additional samples to consider all classes. However, the existing CL methods suffer from two limitations. First, they do not consider the alignment between the class means/prototypes and classifiers, which could lead to poor generalization. Second, existing prototype-based methods treat prototypes as only one additional sample per class, making their influence depend on the number of class instances in a batch and causing unbalanced contributions across classes. To address these limitations, we propose Equilibrium Contrastive Learning (ECL), a supervised CL framework designed to promote geometric equilibrium, where class features, means, and classifiers are harmoniously balanced under data imbalance. The proposed ECL framework uses two main components. First, ECL promotes the representation geometric equilibrium (i.e., a regular simplex geometry characterized by collapsed class samples and uniformly distributed class means), while balancing the contributions of class-average features and class prototypes. Second, ECL establishes a classifier-class center geometric equilibrium by aligning classifier weights and class prototypes. We ran experiments with three long-tailed datasets, the CIFAR-10(0)-LT, ImageNet-LT, and the two imbalanced medical datasets, the ISIC 2019 and our constructed LCCT dataset. Results show that ECL outperforms existing SOTA supervised CL methods designed for imbalanced classification.

翻译：对比学习（CL）是图像分类中的主流技术，但在不平衡数据集上其性能表现有限。最近，一些监督式CL方法被提出，旨在促进表示空间中理想的规则单形几何构型——其特征为类内特征坍缩和均匀的类间均值间距，尤其适用于不平衡数据集。具体而言，现有的基于原型的方法将类原型作为额外样本以考虑所有类别。然而，现有CL方法存在两个局限性。首先，它们未考虑类均值/原型与分类器之间的对齐，这可能导致泛化性能不佳。其次，现有的基于原型的方法将原型仅视为每类的一个额外样本，使得其影响依赖于批次中类实例的数量，并导致不同类别间的贡献不平衡。为解决这些局限性，我们提出均衡对比学习（ECL），这是一种旨在促进几何均衡的监督式CL框架，其中类别特征、均值和分类器在数据不平衡条件下实现和谐平衡。所提出的ECL框架包含两个主要组件。首先，ECL促进表示几何均衡（即由坍缩的类样本和均匀分布的类均值所表征的规则单形几何），同时平衡类平均特征和类原型的贡献。其次，ECL通过对齐分类器权重和类原型，建立分类器-类中心几何均衡。我们在三个长尾数据集（CIFAR-10(0)-LT、ImageNet-LT）以及两个不平衡医学数据集（ISIC 2019和我们构建的LCCT数据集）上进行了实验。结果表明，ECL在性能上优于现有的专为不平衡分类设计的SOTA监督式CL方法。