Self-Supervised Learning

Self-supervised learning is a machine learning approach where models learn to understand data by predicting parts of it from other parts, without needing labeled examples. For instance, a model might learn to fill in missing words in a sentence or recognize parts of an image. This process allows the model to develop useful representations of data by extracting patterns and structure on its own. It’s a way for AI systems to learn efficiently from large amounts of unlabeled data, reducing the need for expensive manual labeling, and enabling applications like language understanding and image recognition.