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 1141–1150 of 4856 papers

Title	Date	Tasks	Status	Hype
Anomaly Detection in Dynamic Graphs: A Comprehensive Survey	May 31, 2024	Anomaly DetectionGraph Mining	—Unverified	0
Stochastic Earned Value Analysis using Monte Carlo Simulation and Statistical Learning Techniques	May 31, 2024	Anomaly Detection	—Unverified	0
From Zero to Hero: Cold-Start Anomaly Detection	May 30, 2024	Anomaly DetectionCold-Start Anomaly Detection	CodeCode Available	0
Performance Examination of Symbolic Aggregate Approximation in IoT Applications	May 30, 2024	Anomaly DetectionDimensionality Reduction	—Unverified	0
Joint Selective State Space Model and Detrending for Robust Time Series Anomaly Detection	May 30, 2024	Anomaly DetectionTime Series	CodeCode Available	0
Segment, Shuffle, and Stitch: A Simple Layer for Improving Time-Series Representations	May 30, 2024	Anomaly DetectionRepresentation Learning	CodeCode Available	1
Video Anomaly Detection in 10 Years: A Survey and Outlook	May 29, 2024	Anomaly DetectionSurvey	—Unverified	0
Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data	May 29, 2024	Anomaly DetectionSemi-supervised Anomaly Detection	CodeCode Available	0
Anomaly Detection by Context Contrasting	May 29, 2024	Anomaly DetectionSelf-Supervised Anomaly Detection	—Unverified	0
A Mallows-like Criterion for Anomaly Detection with Random Forest Implementation	May 29, 2024	Anomaly Detection	—Unverified	0

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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