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.

通过预测视图分配的支持样本（PAWS）的新方法，通过使用软伪标签来将已标记样本的表示与未标记样本的视图表示进行比较，从而将带标签样本无参数地纳入半监督自我监督训练方法中，并强制确保单一未标记实例的不同视图分配相似的伪标签。PAWS 不仅在架构方面优于其他半监督方法，而且需要比以前的最佳方法少 4 倍到 12 倍的训练时间。