Self-Supervised Representation Learning: Introduction, Advances and Challenges

TL;DR

Self-supervised learning enables effective deep feature extraction across multiple data types without large labeled datasets, advancing rapidly and sometimes outperforming supervised methods, but faces ongoing challenges in transferability and efficiency.

Contribution

This paper provides a comprehensive overview of self-supervised representation learning, including key concepts, approaches, applications, and open challenges in the field.

Findings

Self-supervised methods have achieved state-of-the-art results across various data modalities.

These methods reduce reliance on large annotated datasets, easing deployment barriers.

Open challenges include transferability, compute cost, and practical workflows.

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.