  Considerable progress has been made in domain generalization (DG) which aims
to learn a generalizable model from multiple well-annotated source domains to
unknown target domains. However, it can be prohibitively expensive to obtain
sufficient annotation for source datasets in many real scenarios. To escape
from the dilemma between domain generalization and annotation costs, in this
paper, we introduce a novel task named label-efficient domain generalization
(LEDG) to enable model generalization with label-limited source domains. To
address this challenging task, we propose a novel framework called
Collaborative Exploration and Generalization (CEG) which jointly optimizes
active exploration and semi-supervised generalization. Specifically, in active
exploration, to explore class and domain discriminability while avoiding
information divergence and redundancy, we query the labels of the samples with
the highest overall ranking of class uncertainty, domain representativeness,
and information diversity. In semi-supervised generalization, we design
MixUp-based intra- and inter-domain knowledge augmentation to expand domain
knowledge and generalize domain invariance. We unify active exploration and
semi-supervised generalization in a collaborative way and promote mutual
enhancement between them, boosting model generalization with limited
annotation. Extensive experiments show that CEG yields superior generalization
performance. In particular, CEG can even use only 5% data annotation budget to
achieve competitive results compared to the previous DG methods with fully
labeled data on PACS dataset.
