Study of Self-Supervised Pretraining Techniques to Improve Supervised Learning

Abstract

Self-supervised learning (SSL) has emerged as a powerful approach to pretrain models without the need for labeled data, offering a promising avenue for improving supervised learning tasks. This study explores various self-supervised pretraining techniques and their impact on enhancing supervised learning performance across different domains. By leveraging large volumes of unlabeled data, SSL methods can learn rich representations that are later fine-tuned for specific downstream tasks with labeled data. The research investigates several state-of-the-art SSL strategies, including contrastive learning, masked prediction, and clustering-based approaches, assessing their effectiveness in improving model generalization, robustness, and efficiency when paired with traditional supervised learning frameworks. Our experimental results show that models pretrained with self-supervised techniques consistently outperform those trained from scratch or with purely supervised methods, particularly in scenarios with limited labeled data. These findings highlight the potential of self-supervised pretraining as a scalable and data-efficient solution for improving the performance of supervised learning in real-world applications.