One-shot Unsupervised Domain Adaptation

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

7 shown of 7 papers with code (10 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.

• Domain Adaptation with a Single Vision-Language Embedding
  28 Oct 2024 1 repository listed
  Domain adaptation has been extensively investigated in computer vision but still requires access to target data at the training time, which might be difficult to obtain in some uncommon conditions.
• Learnable Data Augmentation for One-Shot Unsupervised Domain Adaptation
  3 Oct 2023 1 repository listed
  This paper presents a classification framework based on learnable data augmentation to tackle the One-Shot Unsupervised Domain Adaptation (OS-UDA) problem.
• One-shot Unsupervised Domain Adaptation with Personalized Diffusion Models
  31 Mar 2023 1 repository listed
  Departing from the common notion of transferring only the target ``texture'' information, we leverage text-to-image diffusion models (e.
• PODA: Prompt-driven Zero-shot Domain Adaptation
  1 Jan 2023 1 repository listed
  In this paper, we propose the task of 'Prompt-driven Zero-shot Domain Adaptation', where we adapt a model trained on a source domain using only a general description in natural language of the target domain, i.
• Semantic Self-adaptation: Enhancing Generalization with a Single Sample
  10 Aug 2022 1 repository listed
  The lack of out-of-domain generalization is a critical weakness of deep networks for semantic segmentation.
• Style Mixing and Patchwise Prototypical Matching for One-Shot Unsupervised Domain Adaptive Semantic Segmentation
  9 Dec 2021 1 repository listed
  In this paper, we tackle the problem of one-shot unsupervised domain adaptation (OSUDA) for semantic segmentation where the segmentors only see one unlabeled target image during training.
• Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation
  13 Apr 2020 1 repository listed Syntology ran 4 of 4 samples · 0 unverified · 2 pointer-only (licence)
  We aim at the problem named One-Shot Unsupervised Domain Adaptation.

Syntology lines on 1 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.