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.

通过将受监督的对比损失整合到基于交叉熵的通信中，解决了长尾学习问题。具体而言，使用Rebalanced Contrastive Learning (RCL)方法来提高长尾分类准确度，通过实现几个关键方面，即特征空间均衡、类内紧凑性和正则化。在Balanced Contrastive Learning (BCL) Framework中实施RCL，实验结果表明RCL提供了对BCL框架的丰富嵌入以及更高的准确度。同时，RCL作为一个独立的损失函数，也实现了与最先进方法相当的准确度水平。

带有重新平衡的对比损失的长尾学习