Self-Supervised Learning Explained for Advanced Users

Self-supervised learning is a learning method in which models are trained on unlabeled data by solving automatically generated tasks.

Parts of the data are used as input, while other parts serve as the target, for example predicting missing image regions or completing texts.

This approach enables learning representations without the need for manual labels.

Self-supervised learning often serves as pretraining to initialize models with general knowledge for later, specific tasks.

It is used in areas such as computer vision, natural language processing, and robotics to increase data efficiency and achieve better generalization.