Evaluation, Benchmarking and Application of Self-Supervised Learning

Abstract

Self-supervised learning has emerged as a promising method for unsupervised representation learning, allowing models to acquire meaningful representations from unlabeled data. This paper presents an overview of various approaches to self-supervised learning, emphasizing their efficacy across different domains. We discuss recent advancements and challenges in the field, illustrating how researchers and practitioners can leverage these techniques to unlock new opportunities for learning rich representations without annotated data. This advancement paves the way for more robust and adaptable machine learning systems