  Semi-supervised learning aims to train a model using limited labels.
State-of-the-art semi-supervised methods for image classification such as PAWS
rely on self-supervised representations learned with large-scale unlabeled but
curated data. However, PAWS is often less effective when using real-world
unlabeled data that is uncurated, e.g., contains out-of-class data. We propose
RoPAWS, a robust extension of PAWS that can work with real-world unlabeled
data. We first reinterpret PAWS as a generative classifier that models
densities using kernel density estimation. From this probabilistic perspective,
we calibrate its prediction based on the densities of labeled and unlabeled
data, which leads to a simple closed-form solution from the Bayes' rule. We
demonstrate that RoPAWS significantly improves PAWS for uncurated Semi-iNat by
+5.3% and curated ImageNet by +0.4%.
