Self-supervised learning (SSL) has gained remarkable success, for which
contrastive learning (CL) plays a key role. However, the recent development of
new non-CL frameworks has achieved comparable or better performance with high
improvement potential, prompting researchers to enhance these frameworks
further. Assimilating CL into non-CL frameworks has been thought to be
beneficial, but empirical evidence indicates no visible improvements. In view
of that, this paper proposes a strategy of performing CL along the dimensional
direction instead of along the batch direction as done in conventional
contrastive learning, named Dimensional Contrastive Learning (DimCL). DimCL
aims to enhance the feature diversity, and it can serve as a regularizer to
prior SSL frameworks. DimCL has been found to be effective, and the
hardness-aware property is identified as a critical reason for its success.
Extensive experimental results reveal that assimilating DimCL into SSL
frameworks leads to performance improvement by a non-trivial margin on various
datasets and backbone architectures.

该研究论文提出了沿着维度方向进行对比学习（Dimensional Contrastive Learning，DimCL）的策略，以增强特征多样性并作为自主学习框架的正则化器，实验证明将 DimCL 融入自主学习框架能够在各种数据集和骨干架构上显著提高性能。