  In this work, we investigate the unexplored intersection of domain
generalization (DG) and data-free learning. In particular, we address the
question: How can knowledge contained in models trained on different source
domains be merged into a single model that generalizes well to unseen target
domains, in the absence of source and target domain data? Machine learning
models that can cope with domain shift are essential for real-world scenarios
with often changing data distributions. Prior DG methods typically rely on
using source domain data, making them unsuitable for private decentralized
data. We define the novel problem of Data-Free Domain Generalization (DFDG), a
practical setting where models trained on the source domains separately are
available instead of the original datasets, and investigate how to effectively
solve the domain generalization problem in that case. We propose DEKAN, an
approach that extracts and fuses domain-specific knowledge from the available
teacher models into a student model robust to domain shift. Our empirical
evaluation demonstrates the effectiveness of our method which achieves first
state-of-the-art results in DFDG by significantly outperforming data-free
knowledge distillation and ensemble baselines.
