Self-Supervised Learning (SSL)
Self-Supervised Learning is a paradigm where a model learns representations from unlabeled data by solving a pretext task that uses the data itself as supervision. For example, a model might predict missing parts of an image or future words in a sentence. Once the model has learned rich, general-purpose features from this pretext task, these learned representations can then be fine-tuned with a small amount of labeled data for specific downstream tasks. This avoids the need for massive human-labeled datasets.
It's like learning to identify objects by playing 'spot the difference' or 'fill in the blank' with pictures, then applying that generalized visual understanding to a more specific task like finding cats.
SSL significantly reduces the reliance on expensive labeled data, making powerful AI models accessible in data-scarce domains and for new use cases.
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