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.
Papers
Confidence-Aware and Self-Supervised Image Anomaly Localisation
Johanna P. Müller, Matthew Baugh, Jeremy Tan, Mischa Dombrowski, Bernhard Kainz
Failure-tolerant Distributed Learning for Anomaly Detection in Wireless Networks
Marc Katzef, Andrew C. Cullen, Tansu Alpcan, Christopher Leckie, Justin Kopacz
TSI-GAN: Unsupervised Time Series Anomaly Detection using Convolutional Cycle-Consistent Generative Adversarial Networks
Shyam Sundar Saravanan, Tie Luo, Mao Van Ngo
Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detection
Hui Lv, Zhongqi Yue, Qianru Sun, Bin Luo, Zhen Cui, Hanwang Zhang
One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship Learning
Jingtao Li, Xinyu Wang, Shaoyu Wang, Hengwei Zhao, Liangpei Zhang, Yanfei Zhong
Anomaly Detection in Aeronautics Data with Quantum-compatible Discrete Deep Generative Model
Thomas Templin, Milad Memarzadeh, Walter Vinci, P. Aaron Lott, Ata Akbari Asanjan, Anthony Alexiades Armenakas, Eleanor Rieffel
DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection
Hui Zhang, Zheng Wang, Zuxuan Wu, Yu-Gang Jiang
Wireless Sensor Networks anomaly detection using Machine Learning: A Survey
Ahsnaul Haque, Md Naseef-Ur-Rahman Chowdhury, Hamdy Soliman, Mohammad Sahinur Hossen, Tanjim Fatima, Imtiaz Ahmed
Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection
Cosmin I Bercea, Benedikt Wiestler, Daniel Rueckert, Julia A Schnabel
Understanding the Challenges and Opportunities of Pose-based Anomaly Detection
Ghazal Alinezhad Noghre, Armin Danesh Pazho, Vinit Katariya, Hamed Tabkhi
Automated visual inspection of CMS HGCAL silicon sensor surface using an ensemble of a deep convolutional autoencoder and classifier
Sonja Grönroos, Maurizio Pierini, Nadezda Chernyavskaya
Learning Representation for Anomaly Detection of Vehicle Trajectories
Ruochen Jiao, Juyang Bai, Xiangguo Liu, Takami Sato, Xiaowei Yuan, Qi Alfred Chen, Qi Zhu