Understanding the Role of Equivariance in Self-supervised Learning

• URL: http://arxiv.org/abs/2411.06508v1
• Date: Sun, 10 Nov 2024 16:09:47 GMT
• Title: Understanding the Role of Equivariance in Self-supervised Learning
• Authors: Yifei Wang, Kaiwen Hu, Sharut Gupta, Ziyu Ye, Yisen Wang, Stefanie Jegelka,
• Abstract summary: equivariant self-supervised learning (E-SSL) learns features to be augmentation-aware. We identify a critical explaining-away effect in E-SSL that creates a synergy between the equivariant and classification tasks. We reveal several principles for practical designs of E-SSL.
• Score: 51.56331245499712
• License:
• Abstract: Contrastive learning has been a leading paradigm for self-supervised learning, but it is widely observed that it comes at the price of sacrificing useful features (\eg colors) by being invariant to data augmentations. Given this limitation, there has been a surge of interest in equivariant self-supervised learning (E-SSL) that learns features to be augmentation-aware. However, even for the simplest rotation prediction method, there is a lack of rigorous understanding of why, when, and how E-SSL learns useful features for downstream tasks. To bridge this gap between practice and theory, we establish an information-theoretic perspective to understand the generalization ability of E-SSL. In particular, we identify a critical explaining-away effect in E-SSL that creates a synergy between the equivariant and classification tasks. This synergy effect encourages models to extract class-relevant features to improve its equivariant prediction, which, in turn, benefits downstream tasks requiring semantic features. Based on this perspective, we theoretically analyze the influence of data transformations and reveal several principles for practical designs of E-SSL. Our theory not only aligns well with existing E-SSL methods but also sheds light on new directions by exploring the benefits of model equivariance. We believe that a theoretically grounded understanding on the role of equivariance would inspire more principled and advanced designs in this field. Code is available at https://github.com/kaotty/Understanding-ESSL.

Related papers

• CGLearn: Consistent Gradient-Based Learning for Out-of-Distribution Generalization [0.7366405857677226]
  In this work, we introduce a simple yet powerful approach, CGLearn, which relies on the agreement of gradients across various environments. Our proposed method demonstrates superior performance compared to state-of-the-art methods in both linear and nonlinear settings. Comprehensive experiments on both synthetic and real-world datasets highlight its effectiveness in diverse scenarios.
  arXiv  Detail & Related papers  (2024-11-09T02:36:39Z)
• A Probabilistic Model Behind Self-Supervised Learning [53.64989127914936]
  In self-supervised learning (SSL), representations are learned via an auxiliary task without annotated labels. We present a generative latent variable model for self-supervised learning. We show that several families of discriminative SSL, including contrastive methods, induce a comparable distribution over representations.
  arXiv  Detail & Related papers  (2024-02-02T13:31:17Z)
• Augmentation-aware Self-supervised Learning with Conditioned Projector [6.720605329045581]
  Self-supervised learning (SSL) is a powerful technique for learning from unlabeled data. We propose to foster sensitivity to characteristics in the representation space by modifying the projector network. Our approach, coined Conditional Augmentation-aware Self-supervised Learning (CASSLE), is directly applicable to typical joint-embedding SSL methods.
  arXiv  Detail & Related papers  (2023-05-31T12:24:06Z)
• ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations [30.745749133759304]
  We develop a theoretical framework to analyze the transferability of self-supervised contrastive learning. We show that contrastive learning fails to learn domain-invariant features, which limits its transferability. Based on these theoretical insights, we propose a novel method called Augmentation-robust Contrastive Learning (ArCL)
  arXiv  Detail & Related papers  (2023-03-02T09:26:20Z)
• Weak Augmentation Guided Relational Self-Supervised Learning [80.0680103295137]
  We introduce a novel relational self-supervised learning (ReSSL) framework that learns representations by modeling the relationship between different instances. Our proposed method employs sharpened distribution of pairwise similarities among different instances as textitrelation metric. Experimental results show that our proposed ReSSL substantially outperforms the state-of-the-art methods across different network architectures.
  arXiv  Detail & Related papers  (2022-03-16T16:14:19Z)
• Why Do Self-Supervised Models Transfer? Investigating the Impact of Invariance on Downstream Tasks [79.13089902898848]
  Self-supervised learning is a powerful paradigm for representation learning on unlabelled images. We show that different tasks in computer vision require features to encode different (in)variances.
  arXiv  Detail & Related papers  (2021-11-22T18:16:35Z)
• Equivariant Contrastive Learning [20.369942206674576]
  In state-of-the-art self-supervised learning (SSL) pre-training produces semantically good representations. We extend popular SSL methods to a more general framework which we name Equivariant Self-Supervised Learning (E-SSL) We demonstrate E-SSL's effectiveness empirically on several popular computer vision benchmarks.
  arXiv  Detail & Related papers  (2021-10-28T17:21:33Z)
• ReSSL: Relational Self-Supervised Learning with Weak Augmentation [68.47096022526927]
  Self-supervised learning has achieved great success in learning visual representations without data annotations. We introduce a novel relational SSL paradigm that learns representations by modeling the relationship between different instances. Our proposed ReSSL significantly outperforms the previous state-of-the-art algorithms in terms of both performance and training efficiency.
  arXiv  Detail & Related papers  (2021-07-20T06:53:07Z)
• Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning [96.75889543560497]
  In many real-world problems, collecting a large number of labeled samples is infeasible. Few-shot learning is the dominant approach to address this issue, where the objective is to quickly adapt to novel categories in presence of a limited number of samples. We propose a novel training mechanism that simultaneously enforces equivariance and invariance to a general set of geometric transformations.
  arXiv  Detail & Related papers  (2021-03-01T21:14:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.