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 2631–2640 of 4856 papers

Title	Date	Tasks	Status	Hype
HaloAE: An HaloNet based Local Transformer Auto-Encoder for Anomaly Detection and Localization	Aug 6, 2022	Anomaly DetectionSegmentation	—Unverified	0
Variational Autoencoders for Anomaly Detection in Respiratory Sounds	Aug 5, 2022	Anomaly Detection	—Unverified	0
Convolutional Ensembling based Few-Shot Defect Detection Technique	Aug 5, 2022	Anomaly DetectionDefect Detection	—Unverified	0
Detecting Multivariate Time Series Anomalies with Zero Known Label	Aug 3, 2022	Anomaly DetectionDensity Estimation	CodeCode Available	1
Robust Learning of Deep Time Series Anomaly Detection Models with Contaminated Training Data	Aug 3, 2022	Anomaly DetectionTime Series	—Unverified	0
Robust PCA for Anomaly Detection and Data Imputation in Seasonal Time Series	Aug 3, 2022	Anomaly DetectionImputation	—Unverified	0
Curved Geometric Networks for Visual Anomaly Recognition	Aug 2, 2022	Anomaly DetectionAnomaly Segmentation	—Unverified	0
Control theoretically explainable application of autoencoder methods to fault detection in nonlinear dynamic systems	Aug 2, 2022	Anomaly DetectionFault Detection	—Unverified	0
DSR -- A dual subspace re-projection network for surface anomaly detection	Aug 2, 2022	Anomaly DetectionAnomaly Localization	CodeCode Available	1
A One-Class Classification method based on Expanded Non-Convex Hulls	Aug 1, 2022	Anomaly DetectionOne-Class Classification	CodeCode Available	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	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