Self-supervised learning is a machine learning paradigm and research field focused on training models using unlabeled data by creating auxiliary tasks that automatically generate supervisory signals. This approach investigates methods for learning robust data representations by leveraging the inherent structure within the data itself, thereby significantly reducing or eliminating the need for costly manual annotation and enabling effective pre-training for various downstream tasks.