Weakly Supervised Anomaly Detection

Weakly Supervised Anomaly Detection is a method within the broader category of anomaly detection in time series that leverages partially labeled or coarsely annotated data to learn anomalous patterns, distinguishing itself from fully supervised and unsupervised approaches by relying on limited or imprecise label information for model training.

124

Publications

8.1K

Citations

377

Authors

162

Institutions

Publications per year

2017–2026

118

Authors

377

  1. GP
    Guansong Pang

    Singapore Management University, Singapore

    5 publications · 811 citations

    6

    h-index

  2. MZ
    Muhammad Zaigham Zaheer

    Mohamed bin Zayed University of Artificial Intelligence, United Arab Emirates

    5 publications · 396 citations

    5

    h-index

  3. SL
    Seung‐Ik Lee

    Electronics and Telecommunications Research Institute, South Korea

    4 publications · 372 citations

    4

    h-index

  4. AM
    Arif Mahmood

    Information Technology University, Pakistan

    4 publications · 372 citations

    4

    h-index

  5. YZ
    Yanning Zhang

    Northwestern Polytechnical University, China

    2 publications · 198 citations

    3

    h-index

1–5 of 377

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 152 citations

    6

    h-index

  2. 5

    h-index

  3. 4

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 679 citations

    4

    h-index

  5. IEEE Signal Processing Letters

    4 publications · 262 citations

    4

    h-index

1–5