Self-Supervised Learning
Self-supervised learning allows models to learn from unlabeled data by generating supervisory signals from the data itself. For example, a model might predict missing words in a sentence (like BERT) or predict future frames in a video from past frames. This creates pre-trained models that can then be fine-tuned on smaller, labeled datasets for specific tasks.
It's like learning to read by scrambling sentences and trying to unscramble them, or predicting the next paragraph in a book you're reading, rather than needing a teacher to give you explicit answers for every single word.
Self-supervised learning dramatically reduces the reliance on expensive labeled datasets, unlocking AI capabilities in data-scarce domains and improving data efficiency.
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