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 2021–2030 of 4856 papers

Title	Date	Tasks	Status	Hype
A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution Detection	Jun 6, 2023	Anomaly DetectionOut-of-Distribution Detection	CodeCode Available	0
High-dimensional and Permutation Invariant Anomaly Detection	Jun 6, 2023	Anomaly DetectionDensity Estimation	CodeCode Available	0
A Robust Likelihood Model for Novelty Detection	Jun 6, 2023	Adversarial AttackAnomaly Detection	—Unverified	0
Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers	Jun 6, 2023	Anomaly DetectionAnomaly Localization	—Unverified	0
ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction	Jun 5, 2023	Anomaly DetectionContrastive Learning	CodeCode Available	1
PV Fleet Modeling via Smooth Periodic Gaussian Copula	Jun 5, 2023	Anomaly DetectionTime Series	—Unverified	0
Disaster Anomaly Detector via Deeper FCDDs for Explainable Initial Responses	Jun 5, 2023	Anomaly DetectionDisaster Response	—Unverified	0
Comparative Study on Semi-supervised Learning Applied for Anomaly Detection in Hydraulic Condition Monitoring System	Jun 5, 2023	Anomaly Detection	—Unverified	0
Point Cloud Video Anomaly Detection Based on Point Spatio-Temporal Auto-Encoder	Jun 4, 2023	Anomaly DetectionVideo Anomaly Detection	—Unverified	0
Anomaly Detection Techniques in Smart Grid Systems: A Review	Jun 4, 2023	Anomaly DetectionFault 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