On the Discriminability of Self-Supervised Representation Learning

• URL: http://arxiv.org/abs/2407.13541v1
• Date: Thu, 18 Jul 2024 14:18:03 GMT
• Title: On the Discriminability of Self-Supervised Representation Learning
• Authors: Zeen Song, Wenwen Qiang, Changwen Zheng, Fuchun Sun, Hui Xiong,
• Abstract summary: Self-supervised learning (SSL) has recently achieved significant success in downstream visual tasks. A notable gap still exists between SSL and supervised learning (SL), especially in complex downstream tasks.
• Score: 38.598160031349686
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-supervised learning (SSL) has recently achieved significant success in downstream visual tasks. However, a notable gap still exists between SSL and supervised learning (SL), especially in complex downstream tasks. In this paper, we show that the features learned by SSL methods suffer from the crowding problem, where features of different classes are not distinctly separated, and features within the same class exhibit large intra-class variance. In contrast, SL ensures a clear separation between classes. We analyze this phenomenon and conclude that SSL objectives do not constrain the relationships between different samples and their augmentations. Our theoretical analysis delves into how SSL objectives fail to enforce the necessary constraints between samples and their augmentations, leading to poor performance in complex tasks. We provide a theoretical framework showing that the performance gap between SSL and SL mainly stems from the inability of SSL methods to capture the aggregation of similar augmentations and the separation of dissimilar augmentations. To address this issue, we propose a learnable regulator called Dynamic Semantic Adjuster (DSA). DSA aggregates and separates samples in the feature space while being robust to outliers. Through extensive empirical evaluations on multiple benchmark datasets, we demonstrate the superiority of DSA in enhancing feature aggregation and separation, ultimately closing the performance gap between SSL and SL.

Related papers

• Self-Supervised Anomaly Detection in the Wild: Favor Joint Embeddings Methods [12.277762115388187]
  Self-Supervised Learning (SSL) offers a promising approach by learning robust representations from unlabeled data. This paper provides a comprehensive evaluation of SSL methods for real-world anomaly detection, focusing on sewer infrastructure.
  arXiv  Detail & Related papers  (2024-10-05T21:27:47Z)
• Look Ahead or Look Around? A Theoretical Comparison Between Autoregressive and Masked Pretraining [34.64600580301882]
  We establish the first theoretical comparisons between two leading generative SSL paradigms: autoregressive SSL and masked SSL. In classification tasks, the flexibility of targeted tokens in masked SSL fosters more inter-sample connections. In content generation tasks, the misalignment between the flexible lengths of test samples and the fixed length of unmasked texts hinders its generation performance.
  arXiv  Detail & Related papers  (2024-07-01T03:35:59Z)
• Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning [4.137391543972184]
  Semi-supervised learning (SSL) has witnessed remarkable progress, resulting in numerous method variations. In this paper, we present a novel SSL approach named FineSSL that significantly addresses this limitation by adapting pre-trained foundation models. We demonstrate that FineSSL sets a new state of the art for SSL on multiple benchmark datasets, reduces the training cost by over six times, and can seamlessly integrate various fine-tuning and modern SSL algorithms.
  arXiv  Detail & Related papers  (2024-05-20T03:33:12Z)
• Reverse Engineering Self-Supervised Learning [17.720366509919167]
  Self-supervised learning (SSL) is a powerful tool in machine learning. This paper presents an in-depth empirical analysis of SSL-trained representations.
  arXiv  Detail & Related papers  (2023-05-24T23:15:28Z)
• Collaborative Intelligence Orchestration: Inconsistency-Based Fusion of Semi-Supervised Learning and Active Learning [60.26659373318915]
  Active learning (AL) and semi-supervised learning (SSL) are two effective, but often isolated, means to alleviate the data-hungry problem. We propose an innovative Inconsistency-based virtual aDvErial algorithm to further investigate SSL-AL's potential superiority. Two real-world case studies visualize the practical industrial value of applying and deploying the proposed data sampling algorithm.
  arXiv  Detail & Related papers  (2022-06-07T13:28:43Z)
• Sound and Visual Representation Learning with Multiple Pretraining Tasks [104.11800812671953]
  Self-supervised tasks (SSL) reveal different features from the data. This work aims to combine Multiple SSL tasks (Multi-SSL) that generalizes well for all downstream tasks. Experiments on sound representations demonstrate that Multi-SSL via incremental learning (IL) of SSL tasks outperforms single SSL task models.
  arXiv  Detail & Related papers  (2022-01-04T09:09:38Z)
• Self-supervised Learning is More Robust to Dataset Imbalance [65.84339596595383]
  We investigate self-supervised learning under dataset imbalance. Off-the-shelf self-supervised representations are already more robust to class imbalance than supervised representations. We devise a re-weighted regularization technique that consistently improves the SSL representation quality on imbalanced datasets.
  arXiv  Detail & Related papers  (2021-10-11T06:29:56Z)
• 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)
• On Data-Augmentation and Consistency-Based Semi-Supervised Learning [77.57285768500225]
  Recently proposed consistency-based Semi-Supervised Learning (SSL) methods have advanced the state of the art in several SSL tasks. Despite these advances, the understanding of these methods is still relatively limited.
  arXiv  Detail & Related papers  (2021-01-18T10:12:31Z)

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.