Anomaly Detection
Anomaly detection focuses on identifying unusual patterns or deviations from expected behavior within data, aiming to improve system reliability and safety across diverse applications. Current research emphasizes unsupervised and self-supervised learning approaches, employing architectures like autoencoders, transformers, and graph neural networks, often incorporating techniques such as Bayesian inference and metric learning to enhance robustness and interpretability. The field's significance stems from its broad applicability, ranging from fraud detection and medical diagnosis to industrial process monitoring and network security, with ongoing efforts to develop more efficient, accurate, and explainable methods.
1129papers
Papers - Page 41
August 1, 2023
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July 28, 2023
Anomaly Detection in Industrial Machinery using IoT Devices and Machine Learning: a Systematic Mapping
Sérgio F. Chevtchenko, Elisson da Silva Rocha, Monalisa Cristina Moura Dos Santos, Ricardo Lins Mota, Diego Moura Vieira+2Benchmarking Jetson Edge Devices with an End-to-end Video-based Anomaly Detection System
Hoang Viet Pham, Thinh Gia Tran, Chuong Dinh Le, An Dinh Le, Hien Bich Vo
July 26, 2023
Coupled-Space Attacks against Random-Walk-based Anomaly Detection
Yuni Lai, Marcin Waniek, Liying Li, Jingwen Wu, Yulin Zhu, Tomasz P. Michalak, Talal Rahwan, Kai ZhouEasyNet: An Easy Network for 3D Industrial Anomaly Detection
Ruitao Chen, Guoyang Xie, Jiaqi Liu, Jinbao Wang, Ziqi Luo, Jinfan Wang, Feng Zheng
July 20, 2023
Heuristic Hyperparameter Choice for Image Anomaly Detection
Zeyu Jiang, João P. C. Bertoldo, Etienne DecencièreRepresentation Learning in Anomaly Detection: Successes, Limits and a Grand Challenge
Yedid HoshenIdentifying Performance Issues in Cloud Service Systems Based on Relational-Temporal Features
Wenwei Gu, Jinyang Liu, Zhuangbin Chen, Jianping Zhang, Yuxin Su, Jiazhen Gu, Cong Feng, Zengyin Yang, Yongqiang Yang, Michael LyuEnsemble Learning based Anomaly Detection for IoT Cybersecurity via Bayesian Hyperparameters Sensitivity Analysis
Tin Lai, Farnaz Farid, Abubakar Bello, Fariza Sabrina
July 17, 2023
July 14, 2023
cOOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations
Silvia D. Almeida, Carsten T. Lüth, Tobias Norajitra, Tassilo Wald, Marco Nolden, Paul F. Jaeger, Claus P. Heussel, Jürgen Biederer, Oliver Weinheimer+1Masked Autoencoders for Unsupervised Anomaly Detection in Medical Images
Mariana-Iuliana GeorgescuMultimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection
Alessandro Flaborea, Luca Collorone, Guido D'Amely, Stefano D'Arrigo, Bardh Prenkaj, Fabio Galasso