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 1921–1930 of 4856 papers

Title	Date	Tasks	Status	Hype
Integration of Domain Expert-Centric Ontology Design into the CRISP-DM for Cyber-Physical Production Systems	Jul 21, 2023	Anomaly Detection	—Unverified	0
Ensemble Learning based Anomaly Detection for IoT Cybersecurity via Bayesian Hyperparameters Sensitivity Analysis	Jul 20, 2023	Anomaly DetectionEnsemble Learning	—Unverified	0
Refining the Optimization Target for Automatic Univariate Time Series Anomaly Detection in Monitoring Services	Jul 20, 2023	Anomaly DetectionTime Series	—Unverified	0
Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities	Jul 20, 2023	Anomaly DetectionEvent Detection	—Unverified	0
Identifying Performance Issues in Cloud Service Systems Based on Relational-Temporal Features	Jul 20, 2023	Anomaly DetectionGraph Attention	CodeCode Available	0
Heuristic Hyperparameter Choice for Image Anomaly Detection	Jul 20, 2023	Anomaly DetectionDimensionality Reduction	—Unverified	0
FACADE: A Framework for Adversarial Circuit Anomaly Detection and Evaluation	Jul 20, 2023	Anomaly Detection	—Unverified	0
Optimizing PatchCore for Few/many-shot Anomaly Detection	Jul 20, 2023	Anomaly DetectionAnomaly Segmentation	CodeCode Available	1
Representation Learning in Anomaly Detection: Successes, Limits and a Grand Challenge	Jul 20, 2023	Anomaly DetectionRepresentation Learning	—Unverified	0
BSDM: Background Suppression Diffusion Model for Hyperspectral Anomaly Detection	Jul 19, 2023	Anomaly DetectionDenoising	CodeCode Available	1

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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