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Topic · Artificial Intelligence

Domain Adaptation and Few-Shot Learning

Domain Adaptation and Few-Shot Learning is a research topic within Artificial Intelligence. Science Explorer counts 23k research works in it since 1951. 27.5% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the advances in transfer learning and domain adaptation, including topics such as few-shot learning, unsupervised learning, representation learning, deep networks, meta-learning, visual recognition, semi-supervised learning, and clustering analysis.

  • Transfer Learning
  • Domain Adaptation
  • Few-Shot Learning
  • Unsupervised Learning
  • Representation Learning
  • Deep Networks
  • Meta-Learning
  • Visual Recognition
  • Semi-Supervised Learning
  • Clustering Analysis
Research works
23k
fractional, since 1951
In the world top 10%
6.2k
per year above
Top-10% rate
27.5%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+343%
the tick is no change

Which countries lead Domain Adaptation and Few-Shot Learning research?

By volume, China and the United States publish the most (5.7k and 1.3k works in 2022–2025).

By volume, 2022–2025

  1. 1 China 5.7k works
  2. 2 United States 1.3k works
  3. 3 India 382 works
  4. 4 South Korea 379 works
  5. 5 United Kingdom 345 works
  6. 6 Germany 284 works
  7. 7 Australia 263 works
  8. 8 Japan 241 works
  9. 9 Canada 194 works
  10. 10 Singapore 192 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 61.4%United States: 14.1%India: 4.1%South Korea: 4.1%6 others listed: 16.3%61%largest
China5,727 · 61.4%United States1,313 · 14.1%India382 · 4.1%South Korea379 · 4.1%6 others listed1,520 · 16.3%

Shares of the rows listed above, not of the whole node.

Which institutions lead Domain Adaptation and Few-Shot Learning research?

By volume in 2022–2025, University of Electronic Science and Technology of China publishes the most Domain Adaptation and Few-Shot Learning research, followed by Xidian University and Chinese Academy of Sciences.

By volume, 2022–2025

  1. 1 University of Electronic Science and Technology of ChinaChina 136 works
  2. 2 Xidian UniversityChina 116 works
  3. 3 Chinese Academy of SciencesChina 112 works
  4. 4 Tsinghua UniversityChina 111 works
  5. 5 Zhejiang UniversityChina 104 works
  6. 6 University of Chinese Academy of SciencesChina 98 works
  7. 7 National University of Defense TechnologyChina 95 works
  8. 8 University of Science and Technology of ChinaChina 94 works
  9. 9 Shanghai Jiao Tong UniversityChina 93 works
  10. 10 Northwestern Polytechnical UniversityChina 89 works

Who are the leading researchers in Domain Adaptation and Few-Shot Learning?

The most-cited researchers publishing on Domain Adaptation and Few-Shot Learning include Andrew Zisserman, Geoffrey E. Hinton and Dumitru Erhan.

  1. 1 Andrew Zisserman United Kingdom 25k citations
  2. 2 Geoffrey E. Hinton Canada 22k citations
  3. 3 Dumitru Erhan United States 17k citations
  4. 4 Kaiming He Israel 17k citations
  5. 5 Li Fei-Fei United States 17k citations
  6. 6 Yoshua Bengio Canada 17k citations
  7. 7 Vincent Vanhoucke United States 15k citations
  8. 8 Serge Belongie United States 14k citations
  9. 9 Xiaogang Wang Russia 13k citations
  10. 10 Jitendra Malik United States 12k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Domain Adaptation and Few-Shot Learning research done?

The largest centres of Domain Adaptation and Few-Shot Learning research in 2022–2025 are Beijing (China), Shanghai (China), Xi'an (China) and Nanjing (China). Among places with at least 20 works in it, it is an unusually large share of all research in Shenzhen and Mountain View.

Largest cities, 2022–2025

  1. 1 Beijing China 1.3k works
  2. 2 Shanghai China 431 works
  3. 3 Xi'an China 369 works
  4. 4 Nanjing China 334 works
  5. 5 Guangzhou China 275 works
  6. 6 Shenzhen China 253 works
  7. 7 Hangzhou China 252 works
  8. 8 Chengdu China 246 works
  9. 9 Wuhan China 225 works
  10. 10 Seoul South Korea 209 works

Where it is the local speciality

  1. ShenzhenCN · 252.9 works7.1×
  2. Mountain ViewUS · 38.0 works6.5×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Domain Adaptation and Few-Shot Learning than the world average.

See Domain Adaptation and Few-Shot Learning on the map

Where is the best place to study Domain Adaptation and Few-Shot Learning?

Among universities, judged by research, Xidian University, Nanyang Technological University and Northwestern Polytechnical University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%60%mean 35.64%fractional works in this node (log) →share in the world top 10% →Xidian University: 116, 36.7%Nanyang Technological University: 70, 41.8%Northwestern Polytechnical University: 89, 36.7%Chinese University of Hong Kong: 33, 48.0%Nanjing University: 66, 32.6%Beijing University of Posts and Telecommunications: 88, 22.6%Mohamed bin Zayed University of Artificial Intelligence: 20, 36.8%National University of Defense Technology: 95, 20.5%University of Technology Sydney: 33, 34.6%Hong Kong University of Science and Technology: 33, 46.1%Chinese University o…Nanyang Technologica…Xidian UniversityNorthwestern Polytec…
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Xidian UniversityChina 81.536.7%15.1×116 +360.1%
2 Nanyang Technological UniversitySingapore 73.641.8%8.3×70 +52.3%
3 Northwestern Polytechnical UniversityChina 73.036.7%7.5×89 +586.6%
4 Chinese University of Hong KongHong Kong 71.448.0%4.5×33 +337.7%
5 Nanjing UniversityChina 70.932.6%8.2×66 +261.7%
6 Beijing University of Posts and TelecommunicationsChina 70.322.6%13.7×88 +657.8%
7 Mohamed bin Zayed University of Artificial IntelligenceUnited Arab Emirates 68.736.8%46.9×20 —
8 National University of Defense TechnologyChina 68.620.5%11.6×95 +974.8%
9 University of Technology SydneyAustralia 68.534.6%7.1×33 +321.0%
10 Hong Kong University of Science and TechnologyHong Kong 68.046.1%8.2×33 -11.9%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Domain Adaptation and Few-Shot Learning research growing?

Output in 2018–2022 was 343% higher than in 2013–2017, peaking in 2023. The fastest-growing topics are Domain Adaptation and Few-Shot Learning.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.