Integrating supervised contrastive loss to cross entropy-based communication has recently been proposed as a solution to address the long-tail learning problem. However, when the class imbalance ratio is high, it requires adjusting the supervised contrastive loss to support the tail classes, as the conventional contrastive learning is biased towards head classes by default. To this end, we present Rebalanced Contrastive Learning (RCL), an efficient means to increase the long tail classification accuracy by addressing three main aspects: 1. Feature space balancedness - Equal division of the feature space among all the classes, 2. Intra-Class compactness - Reducing the distance between same-class embeddings, 3. Regularization - Enforcing larger margins for tail classes to reduce overfitting. RCL adopts class frequency-based SoftMax loss balancing to supervised contrastive learning loss and exploits scalar multiplied features fed to the contrastive learning loss to enforce compactness. We implement RCL on the Balanced Contrastive Learning (BCL) Framework, which has the SOTA performance. Our experiments on three benchmark datasets demonstrate the richness of the learnt embeddings and increased top-1 balanced accuracy RCL provides to the BCL framework. We further demonstrate that the performance of RCL as a standalone loss also achieves state-of-the-art level accuracy.

翻译：将监督对比损失整合到基于交叉熵的通信中，近期被提出作为解决长尾学习问题的一种方案。然而，当类别不平衡比例较高时，需要调整监督对比损失以支持尾部类别，因为传统对比学习默认偏向头部类别。为此，我们提出重平衡对比学习（RCL），这是一种有效提高长尾分类准确率的方法，主要通过三个方面实现：1. 特征空间平衡性——在所有类别间均匀划分特征空间；2. 类内紧凑性——减小同类嵌入之间的距离；3. 正则化——为尾部类别施加更大的间隔以减少过拟合。RCL将基于类别频率的SoftMax损失平衡引入监督对比学习损失，并利用经标量乘法的特征输入对比学习损失以增强紧凑性。我们在具有当前最优性能的平衡对比学习（BCL）框架上实现了RCL。在三个基准数据集上的实验表明，RCL为BCL框架提供了更丰富的学习嵌入和更高的Top-1平衡准确率。我们进一步证明，RCL作为独立损失函数时，其性能也达到了当前最先进的准确率水平。