  Ideally, visual learning algorithms should be generalizable, for dealing with
any unseen domain shift when deployed in a new target environment; and
data-efficient, for reducing development costs by using as little labels as
possible. To this end, we study semi-supervised domain generalization (SSDG),
which aims to learn a domain-generalizable model using multi-source,
partially-labeled training data. We design two benchmarks that cover
state-of-the-art methods developed in two related fields, i.e., domain
generalization (DG) and semi-supervised learning (SSL). We find that the DG
methods, which by design are unable to handle unlabeled data, perform poorly
with limited labels in SSDG; the SSL methods, especially FixMatch, obtain much
better results but are still far away from the basic vanilla model trained
using full labels. We propose StyleMatch, a simple approach that extends
FixMatch with a couple of new ingredients tailored for SSDG: 1) stochastic
modeling for reducing overfitting in scarce labels, and 2) multi-view
consistency learning for enhancing domain generalization. Despite the concise
designs, StyleMatch achieves significant improvements in SSDG. We hope our
approach and the comprehensive benchmarks can pave the way for future research
on generalizable and data-efficient learning systems. The source code is
released at \url{https://github.com/KaiyangZhou/ssdg-benchmark}.
