Self-supervised learning

Topic

Self-supervised learning (SSL) is a machine learning paradigm where a model is trained on a task using the data itself to generate supervisory signals, rather than relying on externally-provided labels. By leveraging inherent structures or relationships within unlabeled input data, it creates meaningful training signals to capture essential features. This approach is widely used to pre-train neural networks on large datasets before fine-tuning them for specific downstream tasks.