  Domain generalization (DG), aiming to make models work on unseen domains, is
a surefire way toward general artificial intelligence. Limited by the scale and
diversity of current DG datasets, it is difficult for existing methods to scale
to diverse domains in open-world scenarios (e.g., science fiction and pixelate
style). Therefore, the source-free domain generalization (SFDG) task is
necessary and challenging. To address this issue, we propose an approach based
on large-scale vision-language pretraining models (e.g., CLIP), which exploits
the extensive domain information embedded in it. The proposed scheme generates
diverse prompts from a domain bank that contains many more diverse domains than
existing DG datasets. Furthermore, our method yields domain-unified
representations from these prompts, thus being able to cope with samples from
open-world domains. Extensive experiments on mainstream DG datasets, namely
PACS, VLCS, OfficeHome, and DomainNet, show that the proposed method achieves
competitive performance compared to state-of-the-art (SOTA) DG methods that
require source domain data for training. Besides, we collect a small datasets
consists of two domains to evaluate the open-world domain generalization
ability of the proposed method. The source code and the dataset will be made
publicly available at
https://github.com/muse1998/Source-Free-Domain-Generalization
