PromptStyler: Prompt-driven Style Generation for Source-free Domain
  Generalization
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.
64c75213d68946edada1ab26
Junhyeong Cho
claimed_verified
2023-07-31T07:01:50.880Z
64c7ce24b8685df8001c6e4a
https://cdn-avatars.huggingface.co/v1/production/uploads/64c7ce24b8685df8001c6e4a/Gy7NY2a2nZ01iW27gvRDE.jpeg
Junhyeong Cho
user
jhcho99
64c75213d68946edada1ab27
Gilhyun Nam
64c75213d68946edada1ab28
Sungyeon Kim
admin_assigned
2023-07-31T07:02:41.935Z
6489d84990497fd1a77a4c25
/avatars/f1b65a2b15464f3d50f9e2f3755d5c14.svg
Sungyeon Kim
user
Sungyeon
64c75213d68946edada1ab29
Hunmin Yang
admin_assigned
2023-07-31T07:02:58.020Z
649ad244511bb7cee43b7259
https://cdn-avatars.huggingface.co/v1/production/uploads/649ad244511bb7cee43b7259/ufmVo-FxNMRu0p4ZvClr1.jpeg
Hunmin Yang
user
hoonsyang
64c75213d68946edada1ab2a
Suha Kwak
PromptStyler: Prompt-driven Style Generation for Source-free Domain
  Generalization
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.
PromptStyler simulates style distribution shifts using prompts in a joint vision-language space for source-free domain generalization, achieving top performance on several benchmarks without images.
PromptStyler distribution shifts synthesis style word vectors style-content features joint vision-language space source-free domain generalization PACS VLCS OfficeHome DomainNet