  Contrastive learning is a representation learning method performed by
contrasting a sample to other similar samples so that they are brought closely
together, forming clusters in the feature space. The learning process is
typically conducted using a two-stage training architecture, and it utilizes
the contrastive loss (CL) for its feature learning. Contrastive learning has
been shown to be quite successful in handling imbalanced datasets, in which
some classes are overrepresented while some others are underrepresented.
However, previous studies have not specifically modified CL for imbalanced
datasets. In this work, we introduce an asymmetric version of CL, referred to
as ACL, in order to directly address the problem of class imbalance. In
addition, we propose the asymmetric focal contrastive loss (AFCL) as a further
generalization of both ACL and focal contrastive loss (FCL). Results on the
FMNIST and ISIC 2018 imbalanced datasets show that AFCL is capable of
outperforming CL and FCL in terms of both weighted and unweighted
classification accuracies. In the appendix, we provide a full axiomatic
treatment on entropy, along with complete proofs.
