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

data category: Image classification benchmarks · 0 papers in the last 7 days

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Labelled image collections used to train and compare image classifiers.

Names searched for: Office-31, Office-Home, OfficeHome, VisDA, DomainNet

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 (2026-10-06 to 2026-11-04), ranking #56 of the 255 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#

20172026 (so far)

Papers per publication year and category
Category	2019	2020	2021	2022	2023	2024	2025	2026 (so far)
AI	3	5	7	8	21	42	28	34
Economics	0	0	0	0	1	1	1	1
Health Economics and Policy	0	0	0	0	0	1	1	1
Information Systems	0	0	0	0	0	0	0	2
Operations Research	0	0	0	0	0	0	1	1
Management	0	0	0	0	0	0	0	2
Marketing	0	0	0	0	1	0	0	0
Higher Education and Research	0	0	0	0	0	1	0	0
All categories	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	23
2	International Journal of Computer Vision	19
3	IEEE Transactions on Neural Networks and Learning Systems	10
4	Machine Learning	9
5	Pattern Recognition	8
6	IEEE Transactions on Artificial Intelligence	8
7	Knowledge-Based Systems	7
8	Expert Systems with Applications	7
9	Neural Networks	6
10	IEEE Transactions on Knowledge and Data Engineering	5

Institutions with the most papers, since 2023
#	Institution	Papers
1	Chinese Academy of Sciences	10
2	Nanyang Technological University	8
3	Nanjing University	7
4	Institute of Automation	6
5	Mohamed bin Zayed University of Artificial Intelligence	6
6	Southern University of Science and Technology	6
7	Agency for Science, Technology and Research	5
8	Jilin University	5
9	Shanghai Jiao Tong University	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	25
2	Office-Home	24
3	domain shift	23
4	domain adaptation	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	International Journal of Computer Vision	2024-07-18	Jian Liang, Ran He, Tieniu Tan
2	256	7.9	A Comprehensive Survey on Source-Free Domain Adaptation	IEEE Transactions on Pattern Analysis and Machine Intelligence	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	IEEE Transactions on Pattern Analysis and Machine Intelligence	2024-06-25	Wenke Huang, Mang Ye, Zekun Shi et al.
4	212	5.9	Domain Adaptation via Prompt Learning	IEEE Transactions on Neural Networks and Learning Systems	2023-11-09	Chunjiang Ge, Rui Huang, Mixue Xie et al.
5	184	5.8	Source-free unsupervised domain adaptation: A survey	Neural Networks	2024-03-11	Yuqi Fang, Pew‐Thian Yap, Weili Lin et al.

Archive counts by publication year: a paper counts when its title, abstract or keywords name the dataset, or when OpenAlex's full-text search finds it there (searched for the last 30 days' papers and, by a pass over the past years, for older ones). Names added to the list later are looked for in older papers as the archive is read again.

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. The whole network →

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	Dataset	72	9.0
CIFAR-10 / CIFAR-100	Dataset	35	7.4
MNIST / Fashion-MNIST	Dataset	34	9.0
SVHN	Dataset	26	36.5
Caltech-101 / 256	Dataset	24	47.6
PASCAL VOC	Dataset	15	13.2
MS COCO	Dataset	14	4.4
Fine-grained benchmarks (CUB-200, Stanford Cars, Oxford Flowers)	Dataset	12	15.4
Cityscapes	Dataset	9	8.9
Hugging Face Hub	Dataset	7	4.4
Action recognition (Kinetics, UCF101, HMDB51)	Dataset	6	6.1
Wikipedia	Dataset	5	1.4

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	AI & Society	Abbe Mowshowitz	City College of New York
2026-10-24	Light-Weight Transformer Layers and Prediction Consistency Loss for Source-Free Unsupervised Domain Adaptation	Pattern Recognition	Yeeun Kim, Gyemin Lee	Seoul National University of Science and Technology
2026-10-22	Zero-Shot Domain Generalization via Domain Prompt Learning	International Journal of Computer Vision	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	Expert Systems with Applications	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	International Journal of Information Management	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	Pattern Recognition	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 4, 2026, from https://scholar4ai.com/datasets/domain-adaptation-benchmarks-office-31-office-home-visda-domainnet

@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 = {2026-11-04},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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