Self-supervised learning is a machine learning approach where the system learns to predict parts of the data from other parts without requiring labeled data. This technique enables the model to generate supervisory signals from the data itself, making it particularly valuable in scenarios where labeled datasets are scarce or expensive to obtain. It bridges the gap between supervised and unsupervised learning by utilizing the structure inherent in the data to train deep learning models effectively.
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