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.
翻译:自我监督学习是众所周知的,因为它在代表性学习和各种下游计算机愿景任务方面的出色表现。最近,积极与唯一抵触学习(POCL)取得了可靠的业绩,而不需要建立积极与消极的训练组合。它通过减少对批量规模的依赖减少了记忆要求。POCL方法通常使用单一损失功能来解析差异性代表(DIR),它描述了受不同扭曲影响的正面与面代表的接近性。这一损失功能隐含地使模型能够过滤或忽略受不同扭曲影响的扭曲变异代表(DVR)。然而,现有的POCL方法并未明确强制实施实际有价值的DVR的分解和利用。此外,这些POCL方法被认为对增强战略敏感。为了解决这些限制,我们提议了一个名为扭曲与分解相矛盾学习(DCL)和扭曲性损失(DDL)的新POCL框架(DL) 。我们的方法是首先在模型和地貌质量框架内明确分解和利用DVR(DVR) 。此外,现有的POCL方法并未明确强制执行实际有价值的DVR 。此外,还观察到这些方法对增强代表性框架的总体利用了我们的标准和特征代表能力。</s>