Self-supervised Representation Learning: Investigating self-supervised learning methods for learning representations from unlabeled data efficiently

Abstract

Self-supervised learning has emerged as a powerful approach for learning representations from unlabeled data. By designing pretext tasks, models can learn meaningful representations that transfer well to downstream tasks. This paper provides an overview of self-supervised representation learning, focusing on key methods and recent advancements. We discuss the motivation behind self-supervised learning, the challenges it addresses, and the advantages it offers. We also review popular self-supervised learning approaches, such as contrastive learning, generative modeling, and predictive learning. Furthermore, we examine the applications of self-supervised learning across various domains and highlight future research directions in this field.