Self-Supervised Learning for Diverse Data Types

Abstract

Self-supervised learning (SSL) has emerged as a powerful approach to leverage unlabeled data for model training across various domains. By formulating auxiliary tasks that generate supervisory signals from the data itself, SSL reduces reliance on large labeled datasets. This paper explores SSL methods applied to diverse data types, including images, text, audio, and time-series data. We discuss the underlying principles, common techniques, and specific algorithms for each data type, alongside challenges and future research directions.