  Invariant-based Contrastive Learning (ICL) methods have achieved impressive
performance across various domains. However, the absence of latent space
representation for distortion (augmentation)-related information in the latent
space makes ICL sub-optimal regarding training efficiency and robustness in
downstream tasks. Recent studies suggest that introducing equivariance into
Contrastive Learning (CL) can improve overall performance. In this paper, we
rethink the roles of augmentation strategies and equivariance in improving CL
efficacy. We propose a novel Equivariant-based Contrastive Learning (ECL)
framework, CLeVER (Contrastive Learning Via Equivariant Representation),
compatible with augmentation strategies of arbitrary complexity for various
mainstream CL methods and model frameworks. Experimental results demonstrate
that CLeVER effectively extracts and incorporates equivariant information from
data, thereby improving the training efficiency and robustness of baseline
models in downstream tasks.
