The paper presents PAWS-VMK, an enhanced approach for prototypical semi-supervised learning using frozen foundation models. It outperforms previous methods in SSL and OOD detection, introducing innovative techniques like vMF-SNE pretraining, MixMatch loss, and SKMPS prototype selection. PAWS-VMK achieves remarkable results on CIFAR-10 (99.2%), CIFAR-100 (89.8%), and Food-101 (90.1%) datasets with minimal labeled instances per class. Additionally, it demonstrates efficient OOD sample detection competitive with specialized methods on OpenOOD benchmarks for CIFAR-10 and CIFAR-100.
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arxiv.org
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