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 4161–4170 of 4856 papers

Title	Date	Tasks	Status
AndroShield: Automated Android Applications Vulnerability Detection, a Hybrid Static and Dynamic Analysis Approach	Oct 22, 2019	Anomaly DetectionMobile Security	CodeCode Available
Abnormal Client Behavior Detection in Federated Learning	Oct 22, 2019	Anomaly DetectionFederated Learning	—Unverified
GraphSAC: Detecting anomalies in large-scale graphs	Oct 21, 2019	Anomaly Detection	—Unverified
Sequential Adversarial Anomaly Detection for One-Class Event Data	Oct 21, 2019	Anomaly DetectionPoint Processes	—Unverified
Dimensionality Increment of PMU Data for Anomaly Detection in Low Observability Power Systems	Oct 19, 2019	Anomaly Detection	—Unverified
Multi-level conformal clustering: A distribution-free technique for clustering and anomaly detection	Oct 17, 2019	Anomaly DetectionClustering	—Unverified
Facial Behavior Analysis using 4D Curvature Statistics for Presentation Attack Detection	Oct 14, 2019	Anomaly Detection	CodeCode Available
A predictive model for the identification of learning styles in MOOC environments	Oct 12, 2019	Anomaly DetectionAutomatic Machine Learning Model Selection	CodeCode Available
Neural Memory Plasticity for Anomaly Detection	Oct 12, 2019	Anomaly DetectionEEG	—Unverified
Spectral embedding of weighted graphs	Oct 12, 2019	Anomaly DetectionClustering	—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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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