Source-free Domain Generalization

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Most implemented papers archive 2025-07-28

5 shown of 5 papers with code (5 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• BatStyler: Advancing Multi-category Style Generation for Source-free Domain Generalization
  2 Jan 2025 1 repository listed
  The Coarse Semantic Generation module extracts coarse-grained semantics to prevent the compression of space for style diversity learning in multi-category configuration, while the Uniform Style Generation module…
• PromptTA: Prompt-driven Text Adapter for Source-free Domain Generalization
  21 Sep 2024 1 repository listed
  In this work, we propose Prompt-Driven Text Adapter (PromptTA) method, which is designed to better capture the distribution of style features and employ resampling to ensure thorough coverage of domain knowledge.
• DPStyler: Dynamic PromptStyler for Source-Free Domain Generalization
  25 Mar 2024 1 repository listed
  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…
• PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization
  27 Jul 2023 1 repository listed Syntology ran 0 of 1 samples · 1 unverified
  In a joint vision-language space, a text feature (e.
• Domain-Unified Prompt Representations for Source-Free Domain Generalization
  29 Sep 2022 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  The proposed scheme generates diverse prompts from a domain bank that contains many more diverse domains than existing DG datasets.

Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.