Self-Supervised Learning

machine-learning unsupervised-learning representation-learning

Definition

Self-Supervised Learning

Self-supervised learning is a form of unsupervised learning where the model generates its own supervision signal from the input data. Formally, a pretext task is defined where part of the input is hidden or transformed, and the learner must predict or reconstruct it (e.g., predicting the next word in a sequence or the missing patch in an image).

Learning Objectives

The primary goal is representation learning, where the model identifies an encoding function that captures the intrinsic structure of the data. Unlike supervised learning, which requires external human-provided labels , self-supervised learning derives targets directly from the unlabelled data itself. The resulting features are typically transferred to downstream tasks to improve generalisation or label efficiency.