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 53
November 25, 2022
November 24, 2022
Detecting Anomalies using Generative Adversarial Networks on Images
Rushikesh Zawar, Krupa Bhayani, Neelanjan Bhowmik, Kamlesh Tiwari, Dhiraj SangwanMeta-Learning for Automated Selection of Anomaly Detectors for Semi-Supervised Datasets
David Schubert, Pritha Gupta, Marcel WeverLearning Invariant Rules from Data for Interpretable Anomaly Detection
Cheng Feng, Pingge Hu
November 23, 2022
November 22, 2022
PNI : Industrial Anomaly Detection using Position and Neighborhood Information
Jaehyeok Bae, Jae-Han Lee, Seyun KimU-Flow: A U-shaped Normalizing Flow for Anomaly Detection with Unsupervised Threshold
Matías Tailanian, Álvaro Pardo, Pablo MuséGeneralizable Industrial Visual Anomaly Detection with Self-Induction Vision Transformer
Haiming Yao, Wenyong YuMGADN: A Multi-task Graph Anomaly Detection Network for Multivariate Time Series
Weixuan Xiong, Xiaochen Sun
November 21, 2022
November 20, 2022
November 18, 2022
November 16, 2022
Are we certain it's anomalous?
Alessandro Flaborea, Bardh Prenkaj, Bharti Munjal, Marco Aurelio Sterpa, Dario Aragona, Luca Podo, Fabio GalassoAnomaly Detection via Multi-Scale Contrasted Memory
Loic Jezequel, Ngoc-Son Vu, Jean Beaudet, Aymeric HistaceOn the Connection of Generative Models and Discriminative Models for Anomaly Detection
Jingxuan Pang, Chunguang Li