title: PromptStyler: Prompt-driven Style Generation for Source-free Domain
    Generalization
summary: 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.
paper_authors._id: 64c75213d68946edada1ab26
paper_authors.name: Junhyeong Cho
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-31T07:01:50.880Z
paper_authors._id: 64c7ce24b8685df8001c6e4a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64c7ce24b8685df8001c6e4a/ckTEJcRGXWp9jx5D4j1Ev.png
paper_authors.fullname: Junhyeong Cho
paper_authors.type: user
paper_authors.user: jhcho99
paper_authors._id: 64c75213d68946edada1ab27
paper_authors.name: Gilhyun Nam
paper_authors._id: 64c75213d68946edada1ab28
paper_authors.name: Sungyeon Kim
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-31T07:02:41.935Z
paper_authors._id: 6489d84990497fd1a77a4c25
paper_authors.avatarUrl: /avatars/f1b65a2b15464f3d50f9e2f3755d5c14.svg
paper_authors.fullname: Sungyeon Kim
paper_authors.type: user
paper_authors.user: Sungyeon
paper_authors._id: 64c75213d68946edada1ab29
paper_authors.name: Hunmin Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-31T07:02:58.020Z
paper_authors._id: 649ad244511bb7cee43b7259
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/649ad244511bb7cee43b7259/ufmVo-FxNMRu0p4ZvClr1.jpeg
paper_authors.fullname: Hunmin Yang
paper_authors.type: user
paper_authors.user: hoonsyang
paper_authors._id: 64c75213d68946edada1ab2a
paper_authors.name: Suha Kwak
paper_title: PromptStyler: Prompt-driven Style Generation for Source-free Domain
    Generalization
paper_summary: 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.
paper_ai_keywords: PromptStyler joint vision-language space text feature image features conditions shifts prompts domain generalization style features learnable style word vectors pseudo-words content information style-content features linear Classifier PACS VLCS OfficeHome DomainNet vision-language space