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 4271–4280 of 4856 papers

Title	Date	Tasks	Status	Hype
Normalizing flows for novelty detection in industrial time series data	Jun 17, 2019	Anomaly DetectionNovelty Detection	—Unverified	0
Three-Dimensional Fourier Scattering Transform and Classification of Hyperspectral Images	Jun 17, 2019	Anomaly DetectionClassification	CodeCode Available	0
Anomaly Detection with Joint Representation Learning of Content and Connection	Jun 16, 2019	Anomaly DetectionRepresentation Learning	—Unverified	0
GAN-based Multiple Adjacent Brain MRI Slice Reconstruction for Unsupervised Alzheimer's Disease Diagnosis	Jun 14, 2019	Anomaly DetectionGenerative Adversarial Network	—Unverified	0
Stochastic Proximal AUC Maximization	Jun 14, 2019	Anomaly Detectionimbalanced classification	—Unverified	0
(1 + )-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets	Jun 14, 2019	Anomaly DetectionGeneral Classification	CodeCode Available	0
Anomaly Detection with HMM Gauge Likelihood Analysis	Jun 14, 2019	Anomaly DetectionClustering	—Unverified	0
GluonTS: Probabilistic Time Series Models in Python	Jun 12, 2019	Anomaly DetectionTime Series	CodeCode Available	3
Warping Resilient Scalable Anomaly Detection in Time Series	Jun 12, 2019	Anomaly DetectionComputational Efficiency	—Unverified	0
Challenges in Time-Stamp Aware Anomaly Detection in Traffic Videos	Jun 11, 2019	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	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	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	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	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