Uncertainty Beats Confidence in semi-supervised learning
In the ever-evolving landscape of artificial intelligence, semi-supervised learning (SSL) has emerged as a powerful approach for harnessing the vast potential of unlabeled data. Traditionally, SSL techniques rely heavily on pseudo-labels—model-generated labels for unlabeled samples—and confidence thresholds to determine their reliability. But this paradigm has long suffered from a critical flaw: overconfidence in model predictions […]
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