Self-Supervised Representation Learning with Meta Comprehensive Regularization

Self-Supervised Learning (SSL) methods harness the concept of semantic invariance by utilizing data augmentation strategies to produce similar representations for different deformations of the same input. Essentially, the model captures the shared information among multiple augmented views of samples, while disregarding the non-shared information that may be beneficial for downstream tasks. To address this issue, we introduce a module called CompMod with Meta Comprehensive Regularization (MCR), embedded into existing self-supervised frameworks, to make the learned representations more comprehensive. Specifically, we update our proposed model through a bi-level optimization mechanism, enabling it to capture comprehensive features. Additionally, guided by the constrained extraction of features using maximum entropy coding, the self-supervised learning model learns more comprehensive features on top of learning consistent features. In addition, we provide theoretical support for our proposed method from information theory and causal counterfactual perspective. Experimental results show that our method achieves significant improvement in classification, object detection and semantic segmentation tasks on multiple benchmark datasets.

Authors

• Huijie Guo Beihang University
• Ying Ba Gaoling School of Artificial Intelligence, Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods
• Jie Hu
• Lingyu Si Science and Technology on Integrated Information System Laboratory, Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences
• Wenwen Qiang Science & Technology on Integrated Information System Laboratory, Institute of Software Chinese Academy of Sciences
• Lei Shi State Key Laboratory of Media Convergence and Communication, CUC, Beijing 100024, China State Key Laboratory of Intelligent Game, Yangtze River Delta Research Institute of NPU, Taicang 215400, China

Keywords

• CV: Learning & Optimization for CV
• CV: Representation Learning for Vision
• ML: Classification and Regression
• ML: Representation Learning