  This paper proposes a novel method of learning by predicting view assignments
with support samples (PAWS). The method trains a model to minimize a
consistency loss, which ensures that different views of the same unlabeled
instance are assigned similar pseudo-labels. The pseudo-labels are generated
non-parametrically, by comparing the representations of the image views to
those of a set of randomly sampled labeled images. The distance between the
view representations and labeled representations is used to provide a weighting
over class labels, which we interpret as a soft pseudo-label. By
non-parametrically incorporating labeled samples in this way, PAWS extends the
distance-metric loss used in self-supervised methods such as BYOL and SwAV to
the semi-supervised setting. Despite the simplicity of the approach, PAWS
outperforms other semi-supervised methods across architectures, setting a new
state-of-the-art for a ResNet-50 on ImageNet trained with either 10% or 1% of
the labels, reaching 75.5% and 66.5% top-1 respectively. PAWS requires 4x to
12x less training than the previous best methods.
