Core Concepts
The author introduces PAWS-VMK, a novel semi-supervised approach that leverages foundation models to set new benchmarks in SSL and OOD detection by addressing key challenges in the original PAWS method.
Abstract
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.
Stats
PAWS-VMK sets new benchmarks in semi-supervised learning for CIFAR-10 (99.2%) and CIFAR-100 (89.8%).
The method achieves 93.1/98.0 and 95.2/96.3 on the CIFAR-10 and CIFAR-100 OpenOOD benchmarks.
Quotes
"PAWS-VMK introduces innovative techniques like vMF-SNE pretraining, MixMatch loss, and SKMPS prototype selection."
"Efficiently detects OOD samples competitive with specialized methods on OpenOOD benchmarks."