Self-Supervised Representation Learning: Introduction, advances, and challenges

TLDR

Self‑supervised representation learning enables powerful deep feature learning without large annotated datasets, easing the annotation bottleneck that hampers practical deep‑learning deployment. This article surveys self‑supervised learning, outlining key concepts, the four main families of approaches, their state‑of‑the‑art performance across modalities, and the major open challenges for future research. The authors describe the four families of self‑supervised methods, their application to diverse data types, and practical aspects such as workflow design, transferability of representations, and computational cost. Recent advances show self‑supervised methods achieving performance comparable to or surpassing fully supervised pre‑training across image, video, audio, text, and graph modalities.

Abstract

Self-supervised representation learning methods aim to provide powerful deep feature learning without the requirement of large annotated datasets, thus alleviating the annotation bottleneck that is one of the main barriers to practical deployment of deep learning today. These methods have advanced rapidly in recent years, with their efficacy approaching and sometimes surpassing fully supervised pre-training alternatives across a variety of data modalities including image, video, sound, text and graphs. This article introduces this vibrant area including key concepts, the four main families of approach and associated state of the art, and how self-supervised methods are applied to diverse modalities of data. We further discuss practical considerations including workflows, representation transferability, and compute cost. Finally, we survey the major open challenges in the field that provide fertile ground for future work.