Harnessing Unlabeled Data with Self-Supervised Learning
Madhav Etelvina, Parris Ward, Trina Kolton, Marissa Dawn, Dylan Emmitt, Kiley Liliana, Wiley Humbert, Jenifer Nadine · 2025
The ability to learn from vast amounts of unlabeled data has emerged as a gamechanging approach in machine learning. By generating supervisory signals from the raw data itself, models can effectively capture representations without the need for expensive labeled datasets. This paradigm, known as self-supervised learning (SSL), has garnered significant interest across multiple fields, including computer vision, natural language processing, and robotics. Through a variety of innovative techniques, such as generative models, contrastive learning, and hybrid approaches, SSL has led to the development of highly effective models that perform well on downstream tasks after minimal supervision. This survey offers an in-depth review of SSL, focusing on its underlying methodologies, challenges, and cutting-edge advancements. We explore popular models such as SimCLR, BYOL, BERT, and DINO, and discuss their applications in diverse domains ranging from image recognition to speech processing and autonomous systems. Despite its rapid progress, SSL faces several challenges, including scalability, model stability, and the dependency on well-designed pretext tasks. Finally, we outline promising future directions aimed at enhancing the efficiency, generalization, and robustness of SSL, positioning it as a crucial technology for next-generation machine learning systems.