  Domain Generalization (DG) aims to generalize a model trained on multiple
source domains to an unseen target domain. The source domains always require
precise annotations, which can be cumbersome or even infeasible to obtain in
practice due to the vast amount of data involved. Web data, however, offers an
opportunity to access large amounts of unlabeled data with rich style
information, which can be leveraged to improve DG. From this perspective, we
introduce a novel paradigm of DG, termed as Semi-Supervised Domain
Generalization (SSDG), to explore how the labeled and unlabeled source domains
can interact, and establish two settings, including the close-set and open-set
SSDG. The close-set SSDG is based on existing public DG datasets, while the
open-set SSDG, built on the newly-collected web-crawled datasets, presents a
novel yet realistic challenge that pushes the limits of current technologies. A
natural approach of SSDG is to transfer knowledge from labeled data to
unlabeled data via pseudo labeling, and train the model on both labeled and
pseudo-labeled data for generalization. Since there are conflicting goals
between domain-oriented pseudo labeling and out-of-domain generalization, we
develop a pseudo labeling phase and a generalization phase independently for
SSDG. Unfortunately, due to the large domain gap, the pseudo labels provided in
the pseudo labeling phase inevitably contain noise, which has negative affect
on the subsequent generalization phase. Therefore, to improve the quality of
pseudo labels and further enhance generalizability, we propose a cyclic
learning framework to encourage a positive feedback between these two phases,
utilizing an evolving intermediate domain that bridges the labeled and
unlabeled domains in a curriculum learning manner...
