Self-supervised Learning - Methods and Applications: Analyzing self-supervised learning methods and their applications in training AI models with limited labeled data

Abstract

Self-supervised learning (SSL) has emerged as a powerful paradigm for training AI models with limited labeled data. By leveraging the inherent structure or redundancy in unlabeled data, SSL methods aim to learn meaningful representations that can generalize well to downstream tasks. This paper provides a comprehensive review of SSL methods, focusing on their underlying principles, advantages, and applications. We analyze key SSL techniques, including contrastive learning, generative modeling, and pretext tasks, highlighting their strengths and limitations. Furthermore, we discuss the challenges and future directions in SSL research, such as scalability, robustness, and interpretability. Finally, we showcase various applications of SSL across different domains, including computer vision, natural language processing, and reinforcement learning, demonstrating its potential to revolutionize AI model training in data-constrained scenarios.