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 2
March 24, 2025
CRCL: Causal Representation Consistency Learning for Anomaly Detection in Surveillance Videos
Yang Liu, Hongjin Wang, Zepu Wang, Xiaoguang Zhu, Jing Liu, Peng Sun, Rui Tang, Jianwei Du, Victor C.M. Leung, Liang SongRoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated Data
Xudong Mou, Rui Wang, Bo Li, Tianyu Wo, Jie Sun, Hui Wang, Xudong LiuBeihang University●Zhongguancun Laboratory●Beihang UniversityTowards Training-free Anomaly Detection with Vision and Language Foundation Models
Jinjin Zhang, Guodong Wang, Yizhou Jin, Di HuangBeihang University
March 23, 2025
Anomaly Detection and Localization for Speech Deepfakes via Feature Pyramid Matching
Emma Coletta, Davide Salvi, Viola Negroni, Daniele Ugo Leonzio, Paolo BestaginiPolitecnico di MilanoGLADMamba: Unsupervised Graph-Level Anomaly Detection Powered by Selective State Space Model
Yali Fu, Jindong Li, Qi Wang, Qianli XingJilin University●Hong Kong University of Science and Technology (Guangzhou)●Engineering Research Center of Knowledge-Driven Human-Machine...+1
March 22, 2025
March 19, 2025
Reducing Communication Overhead in Federated Learning for Network Anomaly Detection with Adaptive Client Selection
William Marfo, Deepak Tosh, Shirley Moore, Joshua Suetterlein, Joseph ManzanoUniversity of Texas at El Paso●Pacific Northwest National LaboratoryRobust Distribution Alignment for Industrial Anomaly Detection under Distribution Shift
Jingyi Liao, Xun Xu, Yongyi Su, Rong-Cheng Tu, Yifan Liu, Dacheng Tao, Xulei YangA*STAR●Nanyang Technological University●South China University of Technology●National University of SingaporeLogLLaMA: Transformer-based log anomaly detection with LLaMA
Zhuoyi Yang, Ian G. HarrisIrvinePruning-Based TinyML Optimization of Machine Learning Models for Anomaly Detection in Electric Vehicle Charging Infrastructure
Fatemeh Dehrouyeh, Ibrahim Shaer, Soodeh Nikan, Firouz Badrkhani Ajaei, Abdallah ShamiUniversity of Western Ontario
March 17, 2025
Deep Learning Advancements in Anomaly Detection: A Comprehensive Survey
Haoqi Huang, Ping Wang, Jianhua Pei, Jiacheng Wang, Shahen Alexanian, Dusit NiyatoYork University●Huazhong University of Science and Technology●Nanyang Technological UniversityTriad: Empowering LMM-based Anomaly Detection with Vision Expert-guided Visual Tokenizer and Manufacturing Process
Yuanze Li, Shihao Yuan, Haolin Wang, Qizhang Li, Ming Liu, Chen Xu, Guangming Shi, Wangmeng ZuoHarbin Institute of Technology●Pazhou Lab Huangpu
March 14, 2025
Bottom-up Iterative Anomalous Diffusion Detector (BI-ADD)
Junwoo Park, Nataliya Sokolovska, Clément Cabriel, Ignacio Izeddin, Judith Miné-HattabLCQB●CNRSMulti-View Industrial Anomaly Detection with Epipolar Constrained Cross-View Fusion
Yifan Liu, Xun Xu, Shijie Li, Jingyi Liao, Xulei YangA*STAR●National University of Singapore