Robust Anomaly Detection

Robust anomaly detection aims to reliably identify unusual patterns or events within data, even in the presence of noise, variations, or adversarial attacks. Current research emphasizes developing models resilient to these challenges, focusing on techniques like iterative refinement, diffusion models, and multi-background representation learning, often combined with self-supervised learning or out-of-distribution detection methods. These advancements are crucial for improving the accuracy and reliability of anomaly detection in diverse applications, ranging from industrial quality control and medical diagnosis to particle physics and autonomous systems. The development of robust and generalizable anomaly detection methods is driving significant progress across various scientific fields and practical applications.

Papers

November 10, 2024

Locally Adaptive One-Class Classifier Fusion with Dynamic $\ell$p-Norm Constraints for Robust Anomaly Detection
Sepehr Nourmohammadi, Arda Sarp Yenicesu, Ozgur S. Oguz
Anomaly Detection Participation Constraint Fusion Weight Robust Anomaly Detection One Class Classifier Anomaly Type

October 24, 2024

Graph Pre-Training Models Are Strong Anomaly Detectors
Jiashun Cheng, Zinan Zheng, Yang Liu, Jianheng Tang, Hongwei Wang, Yu Rong, Jia Li, Fugee Tsung
Graph Neural Network Pre Training Graph Anomaly Detection Graph Level Anomaly Detection Robust Anomaly Detection

September 27, 2024

MIMII-Gen: Generative Modeling Approach for Simulated Evaluation of Anomalous Sound Detection System
Harsh Purohit, Tomoya Nishida, Kota Dohi, Takashi Endo, Yohei Kawaguchi
Anomaly Detection Generative Modeling Anomalous Sound Detection Robust Anomaly Detection Machine Sound

August 21, 2024

Self-Supervised Iterative Refinement for Anomaly Detection in Industrial Quality Control
Muhammad Aqeel, Shakiba Sharifi, Marco Cristani, Francesco Setti
Anomaly Detection Iterative Refinement Robust Anomaly Detection Industrial Inspection

August 9, 2024

Adversarially Robust Industrial Anomaly Detection Through Diffusion Model
Yuanpu Cao, Lu Lin, Jinghui Chen
Diffusion Model Adversarial Robustness Industrial Anomaly Detection Robust Anomaly Detection

June 11, 2024

RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image Anomaly Detection
Yuqi Cheng, Yunkang Cao, Rui Chen, Weiming Shen
Native Robustness Anomaly Detection Image Anomaly Detection Robust Anomaly Detection

June 7, 2024

A Recover-then-Discriminate Framework for Robust Anomaly Detection
Peng Xing, Dong Zhang, Jinhui Tang, Zechao li
Anomaly Detection Image Level Representation Robust Anomaly Detection

May 25, 2024

Secure Hierarchical Federated Learning in Vehicular Networks Using Dynamic Client Selection and Anomaly Detection
M. Saeid HaghighiFard, Sinem Coleri
Anomaly Detection Client Selection Vehicular Network Hierarchical Federated Learning Robust Anomaly Detection

February 23, 2024

PUAD: Frustratingly Simple Method for Robust Anomaly Detection
Shota Sugawara, Ryuji Imamura
Anomaly Detection Distribution Detection Anomaly Map Simple Method Robust Anomaly Detection Distribution Detection Method

January 16, 2024

Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning
Abhijith Gandrakota, Lily Zhang, Aahlad Puli, Kyle Cranmer, Jennifer Ngadiuba, Rajesh Ranganath, Nhan Tran
Anomaly Detection Background Generation High Energy Physic Multi Class Anomaly Detection Robust Anomaly Detection

October 11, 2023

A Discrepancy Aware Framework for Robust Anomaly Detection
Yuxuan Cai, Dingkang Liang, Dongliang Luo, Xinwei He, Xin Yang, Xiang Bai
Defect Detection Synthetic Training Data Anomaly Detection Benchmark Discrepancy Aware Robust Anomaly Detection Synthetic Outlier

July 3, 2023

ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection
Yuhang Chen, Chaoyun Zhang, Minghua Ma, Yudong Liu, Ruomeng Ding, Bowen Li, Shilin He, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang
Diffusion Model Anomaly Detection Time Series Imputation Multivariate Time Series Anomaly Detection Robust Anomaly Detection

March 24, 2023

Anomaly Detection under Distribution Shift
Tri Cao, Jiawen Zhu, Guansong Pang
Anomaly Detection Distribution Shift Anomaly Data Robust Anomaly Detection Unified Generalization

May 29, 2022

Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection
Shaoshen Wang, Yanbin Liu, Ling Chen, Chengqi Zhang
Anomaly Detection Unsupervised Anomaly Detection Empirical Risk Minimization Anomaly Detection Benchmark Robust Anomaly Detection

April 3, 2022

Proactive Anomaly Detection for Robot Navigation with Multi-Sensor Fusion
Tianchen Ji, Arun Narenthiran Sivakumar, Girish Chowdhary, Katherine Driggs-Campbell
Anomaly Detection Mobile Robot Robot Navigation Multi Sensor Fusion Log Anomaly Detection Robust Anomaly Detection

March 25, 2022

Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies
Muhammad Zaigham Zaheer, Jin Ha Lee, Arif Mahmood, Marcella Astrid, Seung-Ik Lee
Anomaly Detection Adversarial Training Anomaly Score Anomalous Data Robust Anomaly Detection Class Novelty Detection

March 24, 2022

Bayesian Nonparametric Submodular Video Partition for Robust Anomaly Detection
Hitesh Sapkota, Qi Yu
Video Anomaly Detection Instance Learning Video Anomaly Object Proposal Robust Anomaly Detection

February 6, 2022

Robust Anomaly Detection for Time-series Data
Min Hu, Yi Wang, Xiaowei Feng, Shengchen Zhou, Zhaoyu Wu, Yuan Qin
Time Series Data Extreme Learning Machine Time Series Anomaly Detection Robust Anomaly Detection