  Self-supervised learning is well known for its remarkable performance in
representation learning and various downstream computer vision tasks. Recently,
Positive-pair-Only Contrastive Learning (POCL) has achieved reliable
performance without the need to construct positive-negative training sets. It
reduces memory requirements by lessening the dependency on the batch size. The
POCL method typically uses a single loss function to extract the distortion
invariant representation (DIR) which describes the proximity of positive-pair
representations affected by different distortions. This loss function
implicitly enables the model to filter out or ignore the distortion variant
representation (DVR) affected by different distortions. However, existing POCL
methods do not explicitly enforce the disentanglement and exploitation of the
actually valuable DVR. In addition, these POCL methods have been observed to be
sensitive to augmentation strategies. To address these limitations, we propose
a novel POCL framework named Distortion-Disentangled Contrastive Learning
(DDCL) and a Distortion-Disentangled Loss (DDL). Our approach is the first to
explicitly disentangle and exploit the DVR inside the model and feature stream
to improve the overall representation utilization efficiency, robustness and
representation ability. Experiments carried out demonstrate the superiority of
our framework to Barlow Twins and Simsiam in terms of convergence,
representation quality, and robustness on several benchmark datasets.
