  This paper describes PAWS-VMK, a prototypical deep learning approach that
obtains state-of-the-art results for image classification tasks in both a
semi-supervised learning (SSL) and out-of-distribution (OOD) detection context.
We consider developments in the fields of SSL, OOD detection, and computer
vision foundation models to introduce a number of innovations that connect the
key ideas within these works to create PAWS-VMK. These innovations include (1)
parametric von Mises-Fisher Stochastic Neighbour Embedding (vMF-SNE) to
initialise a projection head for SSL using the high-quality embeddings of the
foundation model; (2) the PAWS-MixMatch loss, that creates more compact
embeddings and obtains higher accuracy in comparison to the consistency loss
used in PAWS and (3) simple $k$-Means prototype selection (SKMPS), a simple
technique that obtains competitive performance with more complex unsupervised
label selection approaches. PAWS-VMK sets new benchmarks in semi-supervised
learning for CIFAR-10 (99.2%) and CIFAR-100 (89.8%) with four labelled
instances per class, and Food-101 (90.1%) with two labelled instances per
class. We also observe that PAWS-VMK can efficiently detect OOD samples in a
manner that is competitive with specialised methods specifically designed for
this purpose, achieving 93.1/98.0 and 95.2/96.3 on the CIFAR-10 and CIFAR-100
OpenOOD benchmarks.
