Source-Free Domain Generalization (SFDG) aims to develop a model that works
for unseen target domains without relying on any source domain. Recent work,
PromptStyler, employs text prompts to simulate different distribution shifts in
the joint vision-language space, allowing the model to generalize effectively
to unseen domains without using any images. However, 1) PromptStyler's style
generation strategy has limitations, as all style patterns are fixed after the
first training phase. This leads to the training set in the second training
phase being restricted to a limited set of styles. Additionally, 2) the frozen
text encoder in PromptStyler result in the encoder's output varying with the
style of the input text prompts, making it difficult for the model to learn
domain-invariant features. In this paper, we introduce Dynamic PromptStyler
(DPStyler), comprising Style Generation and Style Removal modules to address
these issues. The Style Generation module refreshes all styles at every
training epoch, while the Style Removal module eliminates variations in the
encoder's output features caused by input styles. Moreover, since the Style
Generation module, responsible for generating style word vectors using random
sampling or style mixing, makes the model sensitive to input text prompts, we
introduce a model ensemble method to mitigate this sensitivity. Extensive
experiments demonstrate that our framework outperforms state-of-the-art methods
on benchmark datasets.

无源领域普适化的 PromptStyler 模型存在限制，本论文提出 Dynamic PromptStyler (DPStyler) 来解决这些问题，并通过采用 Style Generation module、Style Removal module 和模型集成方法，证明该方法在基准数据集上优于现有的方法。

DPStyler: 自由源领域广义化的动态提示样式

DPStyler: Dynamic PromptStyler 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 通过合成各种样式来模拟联合空间中的各种分布变化，而无需使用任何图像来处理无源域泛化，其方法通过可学习的样式词向量生成各种样式特征，并将样式内容特征与其对应的内容特征在联合视觉语言空间中相邻，从而确保学到的样式不会扭曲内容信息。在 PACS，VLCS，OfficeHome 和 DomainNet 等数据集上，PromptStyler 取得了最先进的结果，尽管它不需要任何图像，并且只需要使用单个 GPU 进行大约 30 分钟的训练。