Self-supervised learning (SSL) trains models to learn robust representations from unlabeled data by solving automatically generated 'pretext tasks'. This approach significantly reduces reliance on costly human annotations, enabling effective model training in data-scarce environments.
Self-supervised learning allows AI models to learn valuable features from vast amounts of data without needing human-provided labels. It achieves this by creating internal learning tasks, which helps models understand patterns and extract robust representations, especially useful when labeled data is scarce or expensive to obtain.
SSL, pretext task learning, unsupervised representation learning
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