PromptStyler: Prompt-driven Style Generation for Source-free Domain
  Generalization
Authors: name: Junhyeong Cho, name: Gilhyun Nam, name: Sungyeon Kim, name: Hunmin Yang, name: Suha Kwak

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). Inspired by this, 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. Our 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, although it does not
require any images and takes just ~30 minutes for training using a single GPU.