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
A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems
Armin Danesh Pazho, Ghazal Alinezhad Noghre, Arnab A Purkayastha, Jagannadh Vempati, Otto Martin, Hamed Tabkhi
Smart Meter Data Anomaly Detection using Variational Recurrent Autoencoders with Attention
Wenjing Dai, Xiufeng Liu, Alfred Heller, Per Sieverts Nielsen
Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays
Yu Cai, Hao Chen, Xin Yang, Yu Zhou, Kwang-Ting Cheng
Progressive GANomaly: Anomaly detection with progressively growing GANs
Djennifer K. Madzia-Madzou, Hugo J. Kuijf
A Unified Model for Multi-class Anomaly Detection
Zhiyuan You, Lei Cui, Yujun Shen, Kai Yang, Xin Lu, Yu Zheng, Xinyi Le
Perturbation Learning Based Anomaly Detection
Jinyu Cai, Jicong Fan
Anomaly Detection with Test Time Augmentation and Consistency Evaluation
Haowei He, Jiaye Teng, Yang Yuan
Early Abnormal Detection of Sewage Pipe Network: Bagging of Various Abnormal Detection Algorithms
Zhen-Yu Zhang, Guo-Xiang Shao, Chun-Ming Qiu, Yue-Jie Hou, En-Ming Zhao, Chi-Chun Zhou