Self-Supervised Learning for Small Data Environments

Self-Supervised Learning (SSL) presents itself as a dominant learning paradigm that allows models to extract valuable information from unlabeled data collections. The general success of SSL in handling large-scale datasets does not address its potential application in limited data settings. This study analyzes SSL approaches designed for small data systems while focusing on their implementation methods in addition to their challenges and developments. This paper investigates three methods to improve learning efficiency through minimal supervision by focusing on data augmentation and contrastive learning and pre training strategies. Experimental studies show that SSL produces superior performance than standard supervised learning approaches when used in limited data circumstances.