Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples

This paper introduces PAWS, a method for semi-supervised learning that combines labeled and unlabeled data to train models efficiently.

PAWS minimizes a consistency loss to ensure similar pseudo-labels for different views of the same image, leveraging labeled samples non-parametrically.

It achieves state-of-the-art results on ImageNet with significantly less training time.