Self-Supervised Representation Learning

Lilian Weng · 6 years ago

Self-supervised learning leverages unlabeled data by creating pretext tasks that generate labels automatically, allowing models to learn useful representations without expensive manual annotation. Various pretext tasks have been developed for images including rotation prediction, patch relationship prediction, jigsaw puzzles, feature counting, and colorization, with learned representations evaluated by fine-tuning on downstream tasks like ImageNet classification. This approach makes it possible to use the vastly larger pool of unlabeled data available on the internet to train models that learn semantic features beneficial for practical applications.