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Revisiting Diffusion Fine-Tuning for Unsupervised Domain Adaptation — arXiv:2609.33716

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.33716
License (as archived): CC0-1.0
Archived: 2026-10-07T23:51:43.858Z
SHA-256 of the archived text: 17c5829546e090c3db6317618dca8cfba7665af4be33ed67a747cd72995c46e7
Stable URL of this page: https://metraipolis.com/documento/dc_218abb830c8b

How to cite

Revisiting Diffusion Fine-Tuning for Unsupervised Domain Adaptation — arXiv:2609.33716 — arXiv (en), https://arxiv.org/abs/2609.33716 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_218abb830c8b (Metraipolis archive dc_218abb830c8b, sha256 17c5829546e090c3db6317618dca8cfba7665af4be33ed67a747cd72995c46e7)

BibTeX

@misc{dc218abb830c8b,
  title = {Revisiting Diffusion Fine-Tuning for Unsupervised Domain Adaptation — arXiv:2609.33716},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.33716},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_218abb830c8b; sha256 17c5829546e090c3db6317618dca8cfba7665af4be33ed67a747cd72995c46e7},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.33716. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Diffusion-based unsupervised domain adaptation (UDA) improves cross-domain transfer by generating target-specific synthetic data for downstream adaptation. Existing methods are largely designed for single-target adaptation: when a model trained on one labeled source domain must be adapted to multiple unlabeled target domains, they typically require separate diffusion fine-tuning for each source--target pair, causing training, storage, and deployment costs to grow with the number of targets. In this paper, we study multi-target data generation for diffusion-based UDA, where a single source-guided diffusion fine-tuning process is reused to generate target-specific synthetic data for multiple target domains. We propose MUSE (Multi-target UDA-oriented Synthesis with Efficient diffusion fine-tuning), a decoupled adaptation framework that separates source-supervised semantic adaptation from target-specific style adaptation. MUSE uses a shared semantic branch updated by labeled source data and target-private style branches specialized to individual target domains, enabling target-specific generation while avoiding repeated source-guided fine-tuning for each target. Experiments on standard UDA benchmarks show that MUSE achieves a stronger accuracy--efficiency trade-off than repeated per-target diffusion adaptation, reducing diffusion fine-tuning cost while improving average target-domain accuracy. The project page is available at this https URL .

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-29 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.33716 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T23:29:27.244Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).