MDGAD: Meta domain generalization for distribution drift in anomaly detection-Reference-Cited by-同舟云学术

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MDGAD: Meta domain generalization for distribution drift in anomaly detection

Published:2023-09 Issue: Volume:550 Page:126483
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Zhao Sinong,Yu Zhaoyang,Marbach Trent G.,Wang Gang,Yin Airu,Zhou Yatao,Liu Xiaoguang

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference51 articles.

1. Deep learning for anomaly detection: A review;Pang;ACM Computing Surveys (CSUR),2021

2. A unifying review of deep and shallow anomaly detection;Ruff;Proc. IEEE,2021

3. A comprehensive survey on graph anomaly detection with deep learning;Ma;IEEE Trans. Knowl. Data Eng.,2021

4. Predicting node failures in an ultra-large-scale cloud computing platform: an aiops solution;Li;ACM Trans. Software Eng. Methodol. (TOSEM),2020

5. Aiops: real-world challenges and research innovations;Dang,2019

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