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.

提出了一种新的基于等变性的对比学习（ECL）框架 CLeVER，兼容各种主流对比学习方法和模型框架的任意复杂的增强策略，并通过从数据中提取和整合等变信息，提高了基线模型在后续任务中的训练效率和鲁棒性。