Beyond Labels: A Comprehensive Review of Self-Supervised Learning and Intrinsic Data Properties

Abstract

Self-supervised learning (SSL) has become a transformative approach in the field of machine learning, offering a powerful means to harness the vast amounts of unlabeled data available across various domains. By creating auxiliary tasks that generate supervisory signals directly from the data, SSL mitigates the dependency on large, labeled datasets, thereby expanding the applicability of machine learning models. This paper provides a comprehensive exploration of SSL techniques applied to diverse data types, including images, text, audio, and time-series data. We delve into the underlying principles that drive SSL, examine common methodologies, and highlight specific algorithms tailored to each data type. Additionally, we address the unique challenges encountered in applying SSL across different domains and propose future research directions that could further enhance the capabilities and effectiveness of SSL. Through this analysis, we underscore SSL's potential to significantly advance the development of robust, generalizable models capable of tackling complex real-world problems.