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Self-Supervised Representation Learning with Meta Comprehensive Regularization

Huijie Guo, Ying Ba, Jie Hu, Lingyu Si, Wenwen Qiang, Lei Shi

TL;DR

This paper tackles the problem that standard self-supervised learning (SSL) often loses task-relevant information due to data augmentations that enforce invariance. It introduces CompMod with Meta Comprehensive Regularization, a plug-in module that fuses multiple augmented views and uses a bi-level optimization plus maximum entropy coding to encourage comprehensive representations. The authors provide information-theoretic and causal counterfactual justifications for why comprehensive features improve downstream performance, and demonstrate consistent gains across classification, object detection, and instance segmentation on diverse datasets. The approach remains compatible with existing SSL frameworks and shows robustness to hyperparameters and augmentation choices, highlighting its practical impact for improving SSL generalization.

Abstract

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 instance segmentation tasks on multiple benchmark datasets.

Self-Supervised Representation Learning with Meta Comprehensive Regularization

TL;DR

This paper tackles the problem that standard self-supervised learning (SSL) often loses task-relevant information due to data augmentations that enforce invariance. It introduces CompMod with Meta Comprehensive Regularization, a plug-in module that fuses multiple augmented views and uses a bi-level optimization plus maximum entropy coding to encourage comprehensive representations. The authors provide information-theoretic and causal counterfactual justifications for why comprehensive features improve downstream performance, and demonstrate consistent gains across classification, object detection, and instance segmentation on diverse datasets. The approach remains compatible with existing SSL frameworks and shows robustness to hyperparameters and augmentation choices, highlighting its practical impact for improving SSL generalization.

Abstract

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 instance segmentation tasks on multiple benchmark datasets.
Paper Structure (19 sections, 2 theorems, 15 equations, 3 figures, 7 tables, 1 algorithm)

This paper contains 19 sections, 2 theorems, 15 equations, 3 figures, 7 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Contrastive Learning
  5. Analysis Based on Information Theory
  6. Meta Comprehensive Regularization
  7. Model Objective
  8. Causal Interpretation
  9. Experimental Results
  10. Experimental Setting
  11. Downstream Tasks
  12. Self-supervised Learning
  13. Semi-supervised Learning
  14. Transfer Learning
  15. Ablation Experiments
  16. ...and 4 more sections

Key Result

Theorem 1

(Task-Relevant information in representations) In contrastive learning, given a random variable $x$ representing the original sample space, two random variables $x_1$ and $x_2$ characterizing the sample space after augmentation, and two random variable $z_1$ and $z_2$ denoting the augmented samples

Figures (3)

  • Figure 1: Loss of task-related information caused by data augmentation in SSL methods. ($a$), the positive sample pair $(x_a,x_b)$ can be obtained from the input $x$ by Random Cropping and Cutout. ($b$) formally presents the semantics related to label in different augmented views, where $h(\cdot)$ represents the amount of attributes related to the label in sample.
  • Figure 2: Illustration of self-supervised representation learning framework with Meta Comprehensive Regularization.
  • Figure 3: Structural causal model of latent variables. We assume that part of the semantic missing in the augmented view $x_1$ and $x_2$ compared to the original view $x$. These exclusive semantic $\bar{c}$ is affected by the semantic $c$ and the applied augmentation strategy $t$.

Theorems & Definitions (3)

  • Definition 1
  • Theorem 1
  • Theorem 2