A Survey of the Self Supervised Learning Mechanisms for Vision Transformers

2024-08-30

arXiv.org

AI Summary

Vision Transformers (ViTs) suffer from heavy reliance on large-scale labeled data, hindering their applicability in few-shot learning scenarios. Method: This paper presents the first systematic survey of self-supervised learning (SSL) for ViTs, proposing a novel methodology taxonomy that unifies pretraining objectives, representation properties, and evaluation dimensions. Through comprehensive literature analysis, it covers mainstream paradigms—including contrastive learning, masked modeling, and feature prediction—and conducts architecture-aware attribution analysis tailored to ViT-specific inductive biases. Contribution/Results: We introduce a structured SSL classification framework that explicitly characterizes trade-offs among data efficiency, transfer performance, and computational cost. Our analysis reveals cross-method performance boundaries and generalization patterns. Furthermore, we advocate for a scalable, reproducible unified evaluation benchmark for ViT-SSL. This work provides both theoretical foundations and practical guidelines for low-resource visual representation learning.

Technology Category

• Computer Vision: Representation Learning for Vision
• Machine Learning: Unsupervised & Self-Supervised Learning
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Deep supervised learning models require high volume of labeled data to attain sufficiently good results. Although, the practice of gathering and annotating such big data is costly and laborious. Recently, the application of self supervised learning (SSL) in vision tasks has gained significant attention. The intuition behind SSL is to exploit the synchronous relationships within the data as a form of self-supervision, which can be versatile. In the current big data era, most of the data is unlabeled, and the success of SSL thus relies in finding ways to utilize this vast amount of unlabeled data available. Thus it is better for deep learning algorithms to reduce reliance on human supervision and instead focus on self-supervision based on the inherent relationships within the data. With the advent of ViTs, which have achieved remarkable results in computer vision, it is crucial to explore and understand the various SSL mechanisms employed for training these models specifically in scenarios where there is limited labelled data available. In this survey, we develop a comprehensive taxonomy of systematically classifying the SSL techniques based upon their representations and pre-training tasks being applied. Additionally, we discuss the motivations behind SSL, review popular pre-training tasks, and highlight the challenges and advancements in this field. Furthermore, we present a comparative analysis of different SSL methods, evaluate their strengths and limitations, and identify potential avenues for future research.

Problem

• Reducing reliance on labeled data for deep learning models
• Exploring self-supervised learning for Vision Transformers (ViTs)
• Classifying SSL techniques and analyzing their effectiveness

Innovation

• Self-supervised learning reduces labeled data dependency.
• Vision Transformers leverage inherent data relationships.
• Taxonomy classifies SSL techniques by representation tasks.