Vol. 8, Special Issue 3 (2019)

Self-supervised learning (SSL) has emerged as a prominent paradigm within the field of machine learning, presenting a unique approach to training models in the absence of labeled data. This review paper delves into the theoretical underpinnings of self-supervised learning, offering a comprehensive survey of diverse methods and their applications. The primary objective is to provide a holistic understanding of the evolving landscape of SSL, exploring the theoretical frameworks that drive its effectiveness and versatility.

The survey begins by elucidating the fundamental principles that distinguish SSL from traditional supervised learning paradigms. It discusses the key concepts of pretext tasks, where the model is trained to predict certain aspects of the input data, and subsequent downstream tasks, which leverage the learned representations for specific applications. Various theoretical perspectives are explored, ranging from information theory to cognitive science, shedding light on the underlying mechanisms that enable self-supervised models to learn meaningful representations.

A critical analysis of prominent SSL methods follows, categorizing them based on the nature of pretext tasks, such as contrastive learning, generative modeling, and predictive learning. Each category is scrutinized in terms of theoretical motivations, algorithmic implementations, and empirical successes. The survey extends its focus to real-world applications across domains such as computer vision, natural language processing, and audio signal processing, illustrating the adaptability and efficacy of SSL methodologies.

Furthermore, the paper addresses challenges and open questions within the realm of SSL, paving the way for future research directions. It emphasizes the need for a unified theoretical framework to guide the development of novel SSL methods and foster a deeper understanding of the learning dynamics involved.