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

November 2, 2023

Time Series Anomaly Detection using Diffusion-based Models
Ioana Pintilie, Andrei Manolache, Florin Brad
Diffusion Model Anomaly Detection Multivariate Time Series Diffusion Based Method Diffusion Based Model

October 31, 2023

Log-based Anomaly Detection of Enterprise Software: An Empirical Study
Nadun Wijesinghe, Hadi Hemmati
Anomaly Detection Empirical Study Open Source State of the Art Anomaly Industrial Datasets Log Based Anomaly Detection

October 29, 2023

Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection
Yuanze Li, Haolin Wang, Shihao Yuan, Ming Liu, Debin Zhao, Yiwen Guo, Chen Xu, Guangming Shi, Wangmeng Zuo
Anomaly Detection Large Multimodal Model Industrial Anomaly Detection Vision Generalist

October 28, 2023

October 27, 2023

Adversarial Anomaly Detection using Gaussian Priors and Nonlinear Anomaly Scores
Fiete Lüer, Tobias Weber, Maxim Dolgich, Christian Böhm
Anomaly Detection Variational Autoencoder Anomaly Score Gaussian Prior

October 26, 2023

October 25, 2023

October 24, 2023

October 23, 2023

CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference
Vo Nguyen Le Duy, Hsuan-Tien Lin, Ichiro Takeuchi
Anomaly Detection Domain Adaptation Scientific Inference Statistical Inference Selective Inference Unveiling Hidden Energy Anomaly

October 22, 2023

Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection
Ruiying Lu, YuJie Wu, Long Tian, Dongsheng Wang, Bo Chen, Xiyang Liu, Ruimin Hu
Anomaly Detection Vector Quantization Hierarchical Transformer Continuous Representation Prototype Based Neural Network Multi Class Unsupervised Anomaly Detection

October 21, 2023

Concept-based Anomaly Detection in Retail Stores for Automatic Correction using Mobile Robots
Aditya Kapoor, Vartika Sengar, Nijil George, Vighnesh Vatsal, Jayavardhana Gubbi, Balamuralidhar P, Arpan Pal
Vision Transformer Anomaly Detection Mobile Robot Outlier Detection Language Correction Based Method Retail Store

October 20, 2023

October 19, 2023

Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection
Jiawen Zhu, Choubo Ding, Yu Tian, Guansong Pang
Anomaly Detection Open Set Anomaly Detection Datasets

Anomaly Detection

Papers

Time Series Anomaly Detection using Diffusion-based Models

Log-based Anomaly Detection of Enterprise Software: An Empirical Study

Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection

Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach

MEDAVET: Traffic Vehicle Anomaly Detection Mechanism based on spatial and temporal structures in vehicle traffic

Adversarial Anomaly Detection using Gaussian Priors and Nonlinear Anomaly Scores

Making the End-User a Priority in Benchmarking: OrionBench for Unsupervised Time Series Anomaly Detection

MIM-GAN-based Anomaly Detection for Multivariate Time Series Data

Detecting stealthy cyberattacks on adaptive cruise control vehicles: A machine learning approach

On Pixel-level Performance Assessment in Anomaly Detection

GADY: Unsupervised Anomaly Detection on Dynamic Graphs

Localizing Anomalies in Critical Infrastructure using Model-Based Drift Explanations

One or Two Things We know about Concept Drift -- A Survey on Monitoring Evolving Environments

Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstruction

CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference

Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection

Concept-based Anomaly Detection in Retail Stores for Automatic Correction using Mobile Robots

An Event based Prediction Suffix Tree

Anomaly Detection of Command Shell Sessions based on DistilBERT: Unsupervised and Supervised Approaches

Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection