Unleash Model Potential: Bootstrapped Meta Self-supervised Learning

• URL: http://arxiv.org/abs/2308.14267v1
• Date: Mon, 28 Aug 2023 02:49:07 GMT
• Title: Unleash Model Potential: Bootstrapped Meta Self-supervised Learning
• Authors: Jingyao Wang, Zeen Song, Wenwen Qiang, Changwen Zheng
• Abstract summary: Long-term goal of machine learning is to learn general visual representations from a small amount of data without supervision. Self-supervised learning and meta-learning are two promising techniques to achieve this goal, but they both only partially capture the advantages. We propose a novel Bootstrapped Meta Self-Supervised Learning framework that aims to simulate the human learning process.
• Score: 12.57396771974944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The long-term goal of machine learning is to learn general visual representations from a small amount of data without supervision, mimicking three advantages of human cognition: i) no need for labels, ii) robustness to data scarcity, and iii) learning from experience. Self-supervised learning and meta-learning are two promising techniques to achieve this goal, but they both only partially capture the advantages and fail to address all the problems. Self-supervised learning struggles to overcome the drawbacks of data scarcity, while ignoring prior knowledge that can facilitate learning and generalization. Meta-learning relies on supervised information and suffers from a bottleneck of insufficient learning. To address these issues, we propose a novel Bootstrapped Meta Self-Supervised Learning (BMSSL) framework that aims to simulate the human learning process. We first analyze the close relationship between meta-learning and self-supervised learning. Based on this insight, we reconstruct tasks to leverage the strengths of both paradigms, achieving advantages i and ii. Moreover, we employ a bi-level optimization framework that alternates between solving specific tasks with a learned ability (first level) and improving this ability (second level), attaining advantage iii. To fully harness its power, we introduce a bootstrapped target based on meta-gradient to make the model its own teacher. We validate the effectiveness of our approach with comprehensive theoretical and empirical study.

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