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The entropy enigma: Success and failure of entropy minimization
Entropy minimization (EM) is frequently used to increase the accuracy of classification
models when they're faced with new data at test time. EM is a self-supervised learning …
models when they're faced with new data at test time. EM is a self-supervised learning …
Complementary benefits of contrastive learning and self-training under distribution shift
Self-training and contrastive learning have emerged as leading techniques for incorporating
unlabeled data, both under distribution shift (unsupervised domain adaptation) and when it …
unlabeled data, both under distribution shift (unsupervised domain adaptation) and when it …