What is self-supervised learning?

Self-supervised learning (SSL) is a machine learning approach where a model learns from unlabeled data by creating its own supervisory signals. It typically involves masking parts of the input data and training the model to predict the masked portions, thereby learning useful representations of the data. Common uses include pre-training models for natural language processing, computer vision, and audio processing, which can then be fine-tuned for specific downstream tasks with labeled data, leading to improved performance and reduced reliance on large labeled datasets.