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

August 2, 2022

July 31, 2022

Scrutinizing Shipment Records To Thwart Illegal Timber Trade
Debanjan Datta, Sathappan Muthiah, John Simeone, Amelia Meadows, Naren Ramakrishnan
Anomaly Detection Unsupervised Anomaly Detection

July 28, 2022

SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation
Yang Zou, Jongheon Jeong, Latha Pemula, Dongqing Zhang, Onkar Dabeer
Anomaly Detection Segmentation Based Approach Industrial Anomaly Detection Visual Anomaly Detection Anomaly Segmentation Self Supervised Technique

July 27, 2022

Concept Drift Challenge in Multimedia Anomaly Detection: A Case Study with Facial Datasets
Pratibha Kumari, Priyankar Choudhary, Pradeep K. Atrey, Mukesh Saini
Anomaly Detection Case Study Concept Drift Video Dataset Face Image Datasets

July 26, 2022

Task Agnostic and Post-hoc Unseen Distribution Detection
Radhika Dua, Seongjun Yang, Yixuan Li, Edward Choi
Anomaly Detection Large Scale Model Ensembling Task Agnostic Unseen Test Distribution Uncertainty Estimation Task

July 25, 2022

July 24, 2022

Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection
Gaoang Wang, Yibing Zhan, Xinchao Wang, Mingli Song, Klara Nahrstedt
Contrastive Learning Anomaly Detection Semi Supervised Unlabeled Sample Anomaly Contamination

July 23, 2022

July 22, 2022

Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly Detection
Zhiwei Yang, Peng Wu, Jing Liu, Xiaotao Liu
Anomaly Detection Aggregation Network Memory Augmented Adaptive Clustering Feature Clustering

July 21, 2022

Comparative Study on Supervised versus Semi-supervised Machine Learning for Anomaly Detection of In-vehicle CAN Network
Yongqi Dong, Kejia Chen, Yinxuan Peng, Zhiyuan Ma
Anomaly Detection Semi Supervised Learning Semi Supervised Network Programming Vehicle Interaction Vehicle Network

July 20, 2022

Unsupervised Industrial Anomaly Detection via Pattern Generative and Contrastive Networks
Jianfeng Huang, Chenyang Li, Yimin Lin, Shiguo Lian
Contrastive Learning Anomaly Detection Industrial Anomaly Detection Anomaly Localization Unsupervised Detection General Anomaly Enhancement Network

July 18, 2022

Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications
Ali Raza, Shujun Li, Kim-Phuc Tran, Ludovic Koehl
Adversarial Attack Anomaly Detection Poisoning Attack Intrusion Detection Data Poisoning Attack Federated Learning Application

July 15, 2022

Registration based Few-Shot Anomaly Detection
Chaoqin Huang, Haoyan Guan, Aofan Jiang, Ya Zhang, Michael Spratling, Yan-Feng Wang
Anomaly Detection Pre Registration Shot Anomaly Detection Image Alignment

July 14, 2022

Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networks
Evan Caville, Wai Weng Lo, Siamak Layeghy, Marius Portmann
Graph Neural Network Anomaly Detection Network Intrusion Detection Graph Neural Based Anomaly Detection

July 13, 2022

Experiments on Anomaly Detection in Autonomous Driving by Forward-Backward Style Transfers
Daniel Bogdoll, Meng Zhang, Maximilian Nitsche, J. Marius Zöllner
Generative Model Autonomous Driving Anomaly Detection Optical Experiment Pixel Level Anomaly

July 9, 2022

On the Robustness and Anomaly Detection of Sparse Neural Networks
Morgane Ayle, Bertrand Charpentier, John Rachwan, Daniel Zügner, Simon Geisler, Stephan Günnemann
Native Robustness Anomaly Detection Structured Sparsity Sparse Neural Network Sparse Subnetworks Robustness Problem

Anomaly Detection

Papers

DSR -- A dual subspace re-projection network for surface anomaly detection

Curved Geometric Networks for Visual Anomaly Recognition

Scrutinizing Shipment Records To Thwart Illegal Timber Trade

SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation

Concept Drift Challenge in Multimedia Anomaly Detection: A Case Study with Facial Datasets

Task Agnostic and Post-hoc Unseen Distribution Detection

Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series

Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection

Hybrid Classifiers for Spatio-temporal Real-time Abnormal Behaviors Detection, Tracking, and Recognition in Massive Hajj Crowds

Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection

A general-purpose method for applying Explainable AI for Anomaly Detection

Anomaly Detection for Fraud in Cryptocurrency Time Series

Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly Detection

Comparative Study on Supervised versus Semi-supervised Machine Learning for Anomaly Detection of In-vehicle CAN Network

Unsupervised Industrial Anomaly Detection via Pattern Generative and Contrastive Networks

Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications

Registration based Few-Shot Anomaly Detection

Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networks

Experiments on Anomaly Detection in Autonomous Driving by Forward-Backward Style Transfers

On the Robustness and Anomaly Detection of Sparse Neural Networks