---
title: "Domain adaptation benchmarks (Office-31, Office-Home…"
description: "From Nov 2023 to Oct 2026, 125 archived papers mentioned Domain adaptation benchmarks (Office-31, Office-Home, VisDA, DomainNet) in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Aug 2024 (9)."
url: https://scholar4ai.com/datasets/domain-adaptation-benchmarks-office-31-office-home-visda-domainnet
markdown_url: https://scholar4ai.com/datasets/domain-adaptation-benchmarks-office-31-office-home-visda-domainnet.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "From Nov 2023 to Oct 2026, 125 archived papers mentioned Domain adaptation benchmarks (Office-31, Office-Home, VisDA, DomainNet) in their title, abstract or keywords (the search of earlier months' full texts is still… (as of 2026-11-05)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-05T06:02:37+09:00
window: 2026-10-07 to 2026-11-05
source: OpenAlex (CC0)
---

# Domain adaptation benchmarks (Office-31, Office-Home, VisDA, DomainNet)

[Browse dataset profiles](https://scholar4ai.com/datasets/directory/page/2.md)

Labelled image collections used to train and compare image classifiers.

- data category: Image classification benchmarks
- 0 papers in the last 7 days
- Names searched for: Office-31, Office-Home, OfficeHome, VisDA, DomainNet

Most-used data: https://scholar4ai.com/rankings/datasets.md · All Image classification benchmarks datasets: https://scholar4ai.com/datasets/image-classification-benchmarks.md



## Key findings

- From Nov 2023 to Oct 2026, 125 archived papers mentioned Domain adaptation benchmarks (Office-31, Office-Home, VisDA, DomainNet) in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Aug 2024 (9).
- Domain adaptation benchmarks (Office-31, Office-Home, VisDA, DomainNet) was mentioned in 6 papers in the last 30 days (2026-10-07 to 2026-11-05), ranking #51 of the 246 datasets mentioned.
- By journal category: AI (5) and Information Systems (1).
- Most of them appeared in Pattern Recognition (2), AI & Society (1) and International Journal of Computer Vision (1).
- From 2017 to 2026 (so far), Domain adaptation benchmarks (Office-31, Office-Home, VisDA, DomainNet) was mentioned in 163 papers in the tracked journals, most of them in AI (148), economics (4) and health economics and policy (3).
- 31 of them were published in 2025, against 0 in 2017.
- Since 2023, the institutions with the most of these papers were Chinese Academy of Sciences (10), Nanyang Technological University (8) and Nanjing University (7).
- The journals publishing most of them were IEEE Transactions on Pattern Analysis and Machine Intelligence (23), International Journal of Computer Vision (19) and IEEE Transactions on Neural Networks and Learning Systems (10).
- The most-cited of them is “A Comprehensive Survey on Test-Time Adaptation Under Distribution Shifts” (International Journal of Computer Vision, 2024), with 285 citations.
- Papers use Domain adaptation benchmarks (Office-31, Office-Home, VisDA, DomainNet) most often with ImageNet (72), CIFAR-10 / CIFAR-100 (35) and MNIST / Fashion-MNIST (34); it has 18 partners in the code and data network, in the group around ImageNet, CIFAR-10 / CIFAR-100, MNIST / Fashion-MNIST.

## Mentions over 36 months

*Papers mentioning it by publication month, Nov 2023 to Oct 2026 (titles, abstracts and keywords; the full texts of earlier months are still being searched)*

| Trend | Nov 23–Oct 24 | Nov 24–Oct 25 | Nov 25–Oct 26 | 36 months |
|---|---:|---:|---:|---:|
|  | 48 | 32 | 45 | 125 |

## Papers per year

*Papers per publication year and category*

| Category | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 (so far) |
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| [AI](https://scholar4ai.com/categories/ai.md) | 0 | 0 | 3 | 5 | 7 | 8 | 21 | 42 | 28 | 34 |
| [Economics](https://scholar4ai.com/categories/economics.md) | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 |
| [Health Economics and Policy](https://scholar4ai.com/categories/health-economics-and-policy.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 |
| [Information Systems](https://scholar4ai.com/categories/information-systems.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| [Operations Research](https://scholar4ai.com/categories/operations-research.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| [Management](https://scholar4ai.com/categories/management.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| [Marketing](https://scholar4ai.com/categories/marketing.md) | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| [Higher Education and Research](https://scholar4ai.com/categories/higher-education-and-research.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| All categories | 0 | 0 | 3 | 5 | 7 | 8 | 23 | 45 | 31 | 41 |

## Where the papers come from

*Journals with the most papers, since 2023*

| # | Journal | Papers |
|---:|---|---:|
| 1 | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 23 |
| 2 | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | 19 |
| 3 | [IEEE Transactions on Neural Networks and Learning Systems](https://scholar4ai.com/journals/ieee-transactions-on-neural-networks-and-learning-systems.md) | 10 |
| 4 | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 9 |
| 5 | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | 8 |
| 6 | [IEEE Transactions on Artificial Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-artificial-intelligence.md) | 8 |
| 7 | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 7 |
| 8 | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 7 |
| 9 | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 6 |
| 10 | [IEEE Transactions on Knowledge and Data Engineering](https://scholar4ai.com/journals/ieee-transactions-on-knowledge-and-data-engineering.md) | 5 |

*Institutions with the most papers, since 2023*

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 10 |
| 2 | [Nanyang Technological University](https://scholar4ai.com/institutions/nanyang-technological-university.md) | 8 |
| 3 | [Nanjing University](https://scholar4ai.com/institutions/nanjing-university.md) | 7 |
| 4 | [Institute of Automation](https://scholar4ai.com/institutions/institute-of-automation.md) | 6 |
| 5 | Mohamed bin Zayed University of Artificial Intelligence | 6 |
| 6 | [Southern University of Science and Technology](https://scholar4ai.com/institutions/southern-university-of-science-and-technology.md) | 6 |
| 7 | Agency for Science, Technology and Research | 5 |
| 8 | [Jilin University](https://scholar4ai.com/institutions/jilin-university.md) | 5 |
| 9 | [Shanghai Jiao Tong University](https://scholar4ai.com/institutions/shanghai-jiao-tong-university.md) | 5 |
| 10 | The Hong Kong University of Science and Technology (Guangzhou) | 5 |

*Countries with the most papers, since 2023*

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 88 |
| 2 | United Kingdom | 17 |
| 3 | Australia | 16 |
| 4 | Hong Kong | 16 |
| 5 | Singapore | 14 |
| 6 | United States | 13 |
| 7 | Germany | 9 |
| 8 | Canada | 8 |
| 9 | United Arab Emirates | 6 |
| 10 | France | 6 |

*Keywords with the most papers, since 2023*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | [domain generalization](https://scholar4ai.com/keywords/domain-generalization.md) | 25 |
| 2 | Office-Home | 24 |
| 3 | domain shift | 23 |
| 4 | [domain adaptation](https://scholar4ai.com/keywords/domain-adaptation.md) | 21 |
| 5 | unsupervised domain adaptation | 20 |
| 6 | Office-31 | 16 |
| 7 | source-free domain adaptation | 16 |
| 8 | domain-invariant features | 15 |
| 9 | CLIP | 14 |
| 10 | DomainNet | 14 |

## Most-cited papers

*Most-cited papers since 2017*

| # | Citations | Per month | Title | Journal | Published | Authors |
|---:|---:|---:|---|---|---|---|
| 1 | 285 | 10.3 | [A Comprehensive Survey on Test-Time Adaptation Under Distribution Shifts](https://doi.org/10.1007/s11263-024-02181-w) | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | 2024-07-18 | Jian Liang, Ran He, Tieniu Tan |
| 2 | 256 | 7.9 | [A Comprehensive Survey on Source-Free Domain Adaptation](https://doi.org/10.1109/tpami.2024.3370978) | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 2024-02-28 | Jingjing Li, Zhiqi Yu, Zhekai Du et al. |
| 3 | 243 | 8.6 | [Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark](https://doi.org/10.1109/tpami.2024.3418862) | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 2024-06-25 | Wenke Huang, Mang Ye, Zekun Shi et al. |
| 4 | 212 | 5.9 | [Domain Adaptation via Prompt Learning](https://doi.org/10.1109/tnnls.2023.3327962) | [IEEE Transactions on Neural Networks and Learning Systems](https://scholar4ai.com/journals/ieee-transactions-on-neural-networks-and-learning-systems.md) | 2023-11-09 | Chunjiang Ge, Rui Huang, Mixue Xie et al. |
| 5 | 184 | 5.8 | [Source-free unsupervised domain adaptation: A survey](https://doi.org/10.1016/j.neunet.2024.106230) | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 2024-03-11 | Yuqi Fang, Pew‐Thian Yap, Weili Lin et al. |

## Used together with

Papers use Domain adaptation benchmarks (Office-31, Office-Home, VisDA, DomainNet) most often with ImageNet (72), CIFAR-10 / CIFAR-100 (35) and MNIST / Fashion-MNIST (34); it has 18 partners in the code and data network, in the group around ImageNet, CIFAR-10 / CIFAR-100, MNIST / Fashion-MNIST.

*What papers use Domain adaptation benchmarks (Office-31, Office-Home, VisDA, DomainNet) together with (at least 3 papers; lift 1 = chance)*

| Used with | Type | Papers using both | Lift |
|---|---|---:|---:|
| [ImageNet](https://scholar4ai.com/datasets/imagenet.md) | Dataset | 72 | 9.0 |
| [CIFAR-10 / CIFAR-100](https://scholar4ai.com/datasets/cifar-10-cifar-100.md) | Dataset | 35 | 7.4 |
| [MNIST / Fashion-MNIST](https://scholar4ai.com/datasets/mnist-fashion-mnist.md) | Dataset | 34 | 9.0 |
| [SVHN](https://scholar4ai.com/datasets/svhn.md) | Dataset | 26 | 36.5 |
| [Caltech-101 / 256](https://scholar4ai.com/datasets/caltech-101-256.md) | Dataset | 24 | 47.6 |
| [PASCAL VOC](https://scholar4ai.com/datasets/pascal-voc.md) | Dataset | 15 | 13.2 |
| [MS COCO](https://scholar4ai.com/datasets/ms-coco.md) | Dataset | 14 | 4.4 |
| [Fine-grained benchmarks (CUB-200, Stanford Cars, Oxford Flowers)](https://scholar4ai.com/datasets/fine-grained-benchmarks-cub-200-stanford-cars-oxford-flowers.md) | Dataset | 12 | 15.4 |
| [Cityscapes](https://scholar4ai.com/datasets/cityscapes.md) | Dataset | 9 | 8.9 |
| [Hugging Face Hub](https://scholar4ai.com/datasets/hugging-face-hub.md) | Dataset | 7 | 4.4 |
| [Action recognition (Kinetics, UCF101, HMDB51)](https://scholar4ai.com/datasets/action-recognition-kinetics-ucf101-hmdb51.md) | Dataset | 6 | 6.1 |
| [Wikipedia](https://scholar4ai.com/datasets/wikipedia.md) | Dataset | 5 | 1.4 |

The whole network: https://scholar4ai.com/code/network.md

## Papers

*Papers mentioning Domain adaptation benchmarks (Office-31, Office-Home, VisDA, DomainNet) in the last 30 days, newest first.*

| Date | Title | Journal | Authors | Affiliations |
|---|---|---|---|---|
| 2026-10-25 | [Meeting critical challenges in the age of intelligent artifacts](https://doi.org/10.1007/s00146-026-03389-9) | [AI & Society](https://scholar4ai.com/journals/ai-society.md) | Abbe Mowshowitz | City College of New York |
| 2026-10-24 | [Light-Weight Transformer Layers and Prediction Consistency Loss for Source-Free Unsupervised Domain Adaptation](https://doi.org/10.1016/j.patcog.2026.115176) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Yeeun Kim, Gyemin Lee | Seoul National University of Science and Technology |
| 2026-10-22 | [Zero-Shot Domain Generalization via Domain Prompt Learning](https://doi.org/10.1007/s11263-026-03088-4) | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | Shiming Chen, Salman Khan, Salman Khan et al. | Mohamed bin Zayed University of Artificial Intelligence; Huazhong University of Science and Technology; Australian National University; … |
| 2026-10-19 | [Pre-trained initialization and memory-enhanced correction for source-free universal domain adaptation](https://doi.org/10.1016/j.eswa.2026.134794) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Jianhui Yuan, Shuai Lü, Xinyu Zhang et al. | Jilin University; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University |
| 2026-10-15 | [Integrated cognitive processing model of phishing detection: Mood state as moderating factor in the mindful cognition mediated detection process](https://doi.org/10.1016/j.ijinfomgt.2026.103143) | [International Journal of Information Management](https://scholar4ai.com/journals/international-journal-of-information-management.md) | Debalina Bera, Arindam Majumder, Dan Jong Kim | University of North Carolina at Greensboro; University of North Texas |
| 2026-10-14 | [Mitigating domain shift in federated learning via intra- and inter-domain prototypes](https://doi.org/10.1016/j.patcog.2026.115131) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Huy Q. Le, Ye Lin Tun, Yu Qiao et al. | Kyung Hee University; University of Da Nang; Viện Khoa học và Công nghệ số |

## Cite this page

Scholar4AI. (2026). Domain adaptation benchmarks (Office-31, Office-Home, VisDA, DomainNet). Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/datasets/domain-adaptation-benchmarks-office-31-office-home-visda-domainnet

```bibtex
@misc{scholar4ai-datasets-domain-adaptation-benchmarks-office-31-office-home-visda-domainnet,
  author = {{Scholar4AI}},
  title = {Domain adaptation benchmarks (Office-31, Office-Home, VisDA, DomainNet)},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/datasets/domain-adaptation-benchmarks-office-31-office-home-visda-domainnet}},
  urldate = {2026-11-05},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).