Spectral Ghost in Representation Learning: from Component Analysis to Self-Supervised Learning

TL;DR

This paper develops a spectral analysis framework to unify understanding of self-supervised learning, revealing its spectral essence and guiding the design of more efficient algorithms for representation learning.

Contribution

It introduces a spectral perspective to analyze SSL, providing a unified theoretical foundation and insights for designing better algorithms.

Findings

Spectral analysis reveals the core principles of SSL algorithms.

Unified framework clarifies the theoretical basis of diverse SSL methods.

Guides development of more efficient SSL algorithms.

Abstract

Self-supervised learning (SSL) has improved empirical performance by unleashing the power of unlabeled data for practical applications. Specifically, SSL extracts the representation from massive unlabeled data, which will be transferred to a plenty of down streaming tasks with limited data. The significant improvement on diverse applications of representation learning has attracted increasing attention, resulting in a variety of dramatically different self-supervised learning objectives for representation extraction, with an assortment of learning procedures, but the lack of a clear and unified understanding. Such an absence hampers the ongoing development of representation learning, leaving a theoretical understanding missing, principles for efficient algorithm design unclear, and the use of representation learning methods in practice unjustified. The urgency for a unified framework is further motivated by the rapid growth in representation learning methods. In this paper, we are therefore compelled to develop a principled foundation of representation learning. We first theoretically investigate the sufficiency of the representation from a spectral representation view, which reveals the spectral essence of the existing successful SSL algorithms and paves the path to a unified framework for understanding and analysis. Such a framework work also inspires the development of more efficient and easy-to-use representation learning algorithms with principled way in real-world applications.