Abstract

Self-supervised learning (SSL) is an emerging paradigm in machine learning that bridges the gap between supervised and unsupervised learning by allowing models to learn from unlabeled data. The core idea behind SSL is to generate supervisory signals from the data itself, thereby reducing the dependency on large labeled datasets. This paper explores the evolution of self-supervised learning, its underlying principles, key techniques, and recent advancements that make it a promising approach for the development of AI models with minimal labeled data. We discuss the applications of SSL in various domains, such as natural language processing, computer vision, and speech recognition, and its potential to revolutionize industries that suffer from the scarcity of labeled data. Furthermore, we present challenges and future research directions in SSL, including the trade-offs between performance and label efficiency, generalization across tasks, and scalability for large datasets.