What is the concept of self-supervised learning, and how does it differ from supervised and unsupervised learning?

Self-supervised learning is a form of unsupervised learning where the model generates its own training labels from the input data. It differs from supervised learning, where training data is labeled by humans, and unsupervised learning, where no labels are provided. In self-supervised learning, the model is trained to predict certain parts of the input data from other parts, leading to the learning of useful representations.