Anomaly Detection

Anomaly Detection is a binary classification identifying unusual or unexpected patterns in a dataset, which deviate significantly from the majority of the data. The goal of anomaly detection is to identify such anomalies, which could represent errors, fraud, or other types of unusual events, and flag them for further investigation.

[Image source]: GAN-based Anomaly Detection in Imbalance Problems

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1351–1360 of 4856 papers

Title	Date	Tasks	Status
ADs: Active Data-sharing for Data Quality Assurance in Advanced Manufacturing Systems	Mar 31, 2024	Anomaly Detection	—Unverified
Synthetic Time Series for Anomaly Detection in Cloud Microservices	Jul 21, 2024	Anomaly DetectionCloud Computing	—Unverified
DeepTimeAnomalyViz: A Tool for Visualizing and Post-processing Deep Learning Anomaly Detection Results for Industrial Time-Series	Sep 21, 2021	Anomaly DetectionTime Series	—Unverified
Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework	Dec 9, 2020	Anomaly Detection	—Unverified
Defending Water Treatment Networks: Exploiting Spatio-temporal Effects for Cyber Attack Detection	Aug 26, 2020	Anomaly DetectionAttribute	—Unverified
Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation	Nov 13, 2020	Anomaly Detectionfeature selection	—Unverified
EVBattery: A Large-Scale Electric Vehicle Dataset for Battery Health and Capacity Estimation	Jan 28, 2022	Anomaly DetectionCapacity Estimation	—Unverified
Applications of Signature Methods to Market Anomaly Detection	Jan 7, 2022	Anomaly DetectionTime Series	—Unverified
Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection	Sep 13, 2021	Anomaly Detectionspeech-recognition	—Unverified
A Neural Network Anomaly Detector Using the Random Cluster Model	Jan 28, 2015	Anomaly DetectionGeneral Classification	—Unverified

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All datasets MVTec AD VisA MVTec LOCO AD One-class CIFAR-10 CUHK Avenue ShanghaiTech Fishyscapes L&F UCR Anomaly Archive MPDD BTAD One-class CIFAR-100 UBnormal

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	CPR-faster(TensorRT)	FPS	1,016	—	Unverified
2	CPR-fast(TensorRT)	FPS	362	—	Unverified
3	CPR(TensorRT)	FPS	130	—	Unverified
4	GLASS	Detection AUROC	99.9	—	Unverified
5	UniNet	Detection AUROC	99.9	—	Unverified
6	HETMM	Detection AUROC	99.8	—	Unverified
7	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	PBAS	Detection AUROC	99.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UniNet	Detection AUROC	99.8	—	Unverified
2	GLAD	Detection AUROC	99.5	—	Unverified
3	UniNet(model-unified multi-class)	Detection AUROC	99.15	—	Unverified
4	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	DDAD	Detection AUROC	98.9	—	Unverified
6	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
7	DiffusionAD	Detection AUROC	98.8	—	Unverified
8	GLASS	Detection AUROC	98.8	—	Unverified
9	TransFusion	Detection AUROC	98.7	—	Unverified
10	HETMM	Detection AUROC	98.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CSAD	Avg. Detection AUROC	95.3	—	Unverified
2	PSAD	Avg. Detection AUROC	94.9	—	Unverified