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Anomaly Detection Techniques and Applications

Anomaly Detection Techniques and Applications is a research topic within Artificial Intelligence. Science Explorer counts 55k research works in it since 1950. 9.8% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the detection of anomalies in high-dimensional data, particularly in the context of video analysis, surveillance, and time series data. It covers a wide range of techniques including unsupervised learning, outlier detection, deep learning, and novelty detection for identifying abnormal patterns and events.

  • Anomaly Detection
  • Unsupervised
  • Outlier Detection
  • Deep Learning
  • High-Dimensional Data
  • Video Analysis
  • Neural Networks
  • Novelty Detection
  • Surveillance
  • Time Series
Research works
55k
fractional, since 1950
In the world top 10%
5.3k
per year above
Top-10% rate
9.8%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+93%
the tick is no change
Largest producer
China
48% of the 10 largest, 2022-now
Anomaly Detection Techniques and Applications

Which countries lead Anomaly Detection Techniques and Applications research?

By volume, China and India publish the most (9.5k and 3.6k works in 2022-now).

By volume, 2022-now

  1. 1 China 9.5k works
  2. 2 India 3.6k works
  3. 3 United States 2.7k works
  4. 4 South Korea 735 works
  5. 5 Germany 708 works
  6. 6 United Kingdom 640 works
  7. 7 Italy 552 works
  8. 8 Japan 539 works
  9. 9 Canada 453 works
  10. 10 France 450 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: 47.7%India: 18.1%United States: 13.7%South Korea: 3.7%6 others listed: 16.7%48%largest
China9,527 · 47.7%India3,618 · 18.1%United States2,730 · 13.7%South Korea735 · 3.7%6 others listed3,341 · 16.7%

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

Which institutions lead Anomaly Detection Techniques and Applications research?

By volume in 2022-now, Chinese Academy of Sciences publishes the most Anomaly Detection Techniques and Applications research, followed by SRM Institute of Science and Technology and Tsinghua University.

By volume, 2022-now

  1. 1 Chinese Academy of SciencesChina 317 works
  2. 2 SRM Institute of Science and TechnologyIndia 155 works
  3. 3 Tsinghua UniversityChina 141 works
  4. 4 Beijing University of Posts and TelecommunicationsChina 134 works
  5. 5 National University of Defense TechnologyChina 132 works
  6. 6 Beihang UniversityChina 128 works
  7. 7 Zhejiang UniversityChina 125 works
  8. 8 Harbin Institute of TechnologyChina 123 works
  9. 9 Xi'an Jiaotong UniversityChina 121 works
  10. 10 University of Electronic Science and Technology of ChinaChina 120 works

Who are the leading researchers in Anomaly Detection Techniques and Applications?

The most-cited researchers publishing on Anomaly Detection Techniques and Applications include Kaiming He, Yoshua Bengio and Geoffrey E. Hinton.

  1. 1 Kaiming He Israel 562k citations
  2. 2 Yoshua Bengio Canada 476k citations
  3. 3 Geoffrey E. Hinton Canada 454k citations
  4. 4 Robert John Tibshirani United States 396k citations
  5. 5 Ross Girshick Israel 356k citations
  6. 6 Xiangyu Zhang 327k citations
  7. 7 Trevor Hastie United States 301k citations
  8. 8 Jian Sun 281k citations
  9. 9 Xiaogang Wang China 260k citations
  10. 10 Yann LeCun United States 256k citations

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

What are the most cited papers in Anomaly Detection Techniques and Applications?

The most cited paper in Anomaly Detection Techniques and Applications is "Deep learning" (Yann LeCun, Yoshua Bengio, Geoffrey E. Hinton, 2015), with 84,939 citations. Next come "LIBSVM" (41,500) and "SMOTE: Synthetic Minority Over-sampling Technique" (32,759).

Most cited papers
#PaperReleasedCitations
1 Deep learning Yann LeCun, Yoshua Bengio, Geoffrey E. Hinton · Nature Open access 26 May 2015 84,939
2 LIBSVM Chih-Chung Chang, Chih‐Jen Lin · ACM Transactions on Intelligent Systems and Technology Apr 2011 41,500
3 SMOTE: Synthetic Minority Over-sampling Technique Nitesh V. Chawla, Kevin W. Bowyer, Lawrence Hall et al. · Journal of Artificial Intelligence Research Open access Jun 2002 32,759
4 Rethinking the Inception Architecture for Computer Vision Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe et al. Conference paper Jun 2016 31,624
5 MobileNetV2: Inverted Residuals and Linear Bottlenecks Mark Sandler, Andrew Howard, Menglong Zhu et al. Conference paper Jun 2018 26,808
All 98 most cited papers, with filters and sorting

Where is Anomaly Detection Techniques and Applications research done?

The largest centres of Anomaly Detection Techniques and Applications research in 2022-now are Beijing (China), Shanghai (China), Xi'an (China) and Nanjing (China).

Largest cities, 2022-now

  1. 1 BeijingChina 1.9k works
  2. 2 ShanghaiChina 606 works
  3. 3 Xi'anChina 567 works
  4. 4 NanjingChina 513 works
  5. 5 ChennaiIndia 462 works
  6. 6 WuhanChina 415 works
  7. 7 GuangzhouChina 388 works
  8. 8 ChengduChina 380 works
  9. 9 SeoulSouth Korea 351 works
  10. 10 HangzhouChina 347 works
See Anomaly Detection Techniques and Applications on the map

Where is the best place to study Anomaly Detection Techniques and Applications?

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

0%10%20%30%mean 21.26%fractional works in this field (log) →share of work in the world top 10% →Air UniversityNanyang Technologica…Huazhong University …Tsinghua University
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Citation excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1Nanyang Technological UniversitySingapore 67.421.1%3.4×61 +40.2%
2Tsinghua UniversityChina 67.317.1%2.7×141 +47.8%
3Air UniversityPakistan 66.826.7%11.6×15 +217.5%
4Huazhong University of Science and TechnologyChina 66.321.0%2.5×102 +152.3%
5Beihang UniversityChina 66.016.3%5.1×128 +124.8%
6Xi'an Jiaotong UniversityChina 65.317.1%3.0×121 +95.5%
7Princess Nourah bint Abdulrahman UniversitySaudi Arabia 64.327.9%2.8×16 +948.9%
8Zhejiang UniversityChina 64.216.2%2.0×125 +176.1%
9King Saud UniversitySaudi Arabia 64.227.7%1.4×30 +104.1%
10Hong Kong Polytechnic UniversityChina 63.821.5%2.1×40 +163.4%

Universities only. The score blends citation excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Anomaly Detection Techniques and Applications, country by country

Is Anomaly Detection Techniques and Applications research growing?

Output in 2022-2025 was 93% higher than in 2018-2021, peaking in 2024. The fastest-growing topics are Anomaly Detection Techniques and Applications.

still counting19801990200020102020
grewheldshrank

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

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Anomaly Detection Techniques and Applications as a percentage of every work published that year, 0.13% in 2024.

still counting0.050%0.10%0.15%Anomaly Detection Techniques and Applications0.12% in 202519801990200020102020

Fractional works of Anomaly Detection Techniques and Applications over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Anomaly Detection Techniques and Applications9.7% in 202520102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

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.

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