The user wants to understand Self-supervised Learning in the context of Machine Learning Foundations and apply it to practical AI governance or compliance work.
Self-supervised learning is a machine learning approach that creates training signals from unlabeled data itself. It converts aspects of an unsupervised problem into supervised learning by generating surrogate labels, making it useful when labeled data is scarce but unlabeled data is abundant.
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