Since 2021, aggregated from related topics
Self-supervised learning is a type of machine learning technique where a model learns to predict certain characteristics or features of a dataset without explicit supervision or labels. Instead of relying on labeled data, self-supervised learning uses the inherent structure or relationships within the data itself to train the model. This method is particularly useful in scenarios where labeled data is scarce or expensive to obtain. Self-supervised learning has shown promising results in various applications such as natural language processing, computer vision, and speech recognition.