  In a joint vision-language space, a text feature (e.g., from "a photo of a
dog") could effectively represent its relevant image features (e.g., from dog
photos). Also, a recent study has demonstrated the cross-modal transferability
phenomenon of this joint space. From these observations, we propose
PromptStyler which simulates various distribution shifts in the joint space by
synthesizing diverse styles via prompts without using any images to deal with
source-free domain generalization. The proposed method learns to generate a
variety of style features (from "a S* style of a") via learnable style word
vectors for pseudo-words S*. To ensure that learned styles do not distort
content information, we force style-content features (from "a S* style of a
[class]") to be located nearby their corresponding content features (from
"[class]") in the joint vision-language space. After learning style word
vectors, we train a linear classifier using synthesized style-content features.
PromptStyler achieves the state of the art on PACS, VLCS, OfficeHome and
DomainNet, even though it does not require any images for training.
