Self-Supervised Learning Techniques for Large-Scale AI Systems
TL;DR
Experimental results demonstrate that SSL outperforms traditional supervised learning in accuracy, scalability, and transferability, while also reducing data labeling costs.
Abstract
Self-supervised learning (SSL) is transforming artificial intelligence by enabling models to learn from large amounts of unlabeled data. Instead of relying on manual annotations, SSL leverages inherent data patterns to generate pseudo-labels, making it highly scalable and efficient for modern AI systems. Techniques such as contrastive learning, masked modeling, generative pretraining, and clustering have shown strong performance across vision, language, and speech tasks. This study examines key SSL methods, architectures, and training strategies, while addressing challenges like computational cost, feature collapse, and data bias. It proposes a unified framework that combines contrastive and generative approaches for improved efficiency and representation learning. Experimental results demonstrate that SSL outperforms traditional supervised learning in accuracy, scalability, and transferability, while also reducing data labeling costs. Future directions include integrating multimodal, reinforcement, and continual learning to further enhance SSL systems.