Long-Tail Learning with Rebalanced Contrastive Loss
Charika De Alvis Dishanika Denipitiyage Suranga Seneviratne
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.