Self-Supervised Learning is a type of machine learning in which the model is given a task to perform, but is not explicitly given the correct answers. Instead, the model must figure out how to solve the task on its own, using the input data as a guide. The Self-Supervised Learning Framework allows the model to learn more complex patterns and relationships in the data. Overall, it allows machines to learn and adapt in a more autonomous and flexible way.
