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 3661–3670 of 4856 papers

Title	Date	Tasks	Status	Hype
Multiresolution Knowledge Distillation for Anomaly Detection	Nov 22, 2020	Anomaly DetectionAnomaly Localization	CodeCode Available	1
Efficient Consensus Model based on Proximal Gradient Method applied to Convolutional Sparse Problems	Nov 19, 2020	Anomaly DetectionDictionary Learning	—Unverified	0
Preparing Weather Data for Real-Time Building Energy Simulation	Nov 19, 2020	Anomaly DetectionImputation	—Unverified	0
Sub-clusters of Normal Data for Anomaly Detection	Nov 17, 2020	Anomaly DetectionClustering	—Unverified	0
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization	Nov 17, 2020	Anomaly DetectionAnomaly Localization	CodeCode Available	1
Combining GANs and AutoEncoders for Efficient Anomaly Detection	Nov 16, 2020	Adversarial AttackAnomaly Detection	CodeCode Available	1
Learning normal appearance for fetal anomaly screening: Application to the unsupervised detection of Hypoplastic Left Heart Syndrome	Nov 15, 2020	AnatomyAnomaly Detection	—Unverified	0
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning	Nov 15, 2020	Abnormal Event Detection In VideoAnomaly Detection	CodeCode Available	1
Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation	Nov 13, 2020	Anomaly Detectionfeature selection	—Unverified	0
Local Anomaly Detection in Videos using Object-Centric Adversarial Learning	Nov 13, 2020	Anomaly DetectionObject	—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	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