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 2771–2780 of 4856 papers

Title	Date	Tasks	Status	Hype
Neural Contextual Bandits Based Dynamic Sensor Selection for Low-Power Body-Area Networks	May 24, 2022	Anomaly DetectionMulti-Armed Bandits	—Unverified	0
Naive Few-Shot Learning: Uncovering the fluid intelligence of machines	May 24, 2022	Anomaly DetectionFew-Shot Learning	—Unverified	0
Psychotic Relapse Prediction in Schizophrenia Patients using A Mobile Sensing-based Supervised Deep Learning Model	May 24, 2022	Anomaly DetectionDeep Learning	—Unverified	0
GraphAD: A Graph Neural Network for Entity-Wise Multivariate Time-Series Anomaly Detection	May 23, 2022	Anomaly DetectionGraph Neural Network	—Unverified	0
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images	May 23, 2022	Anomaly Detection	CodeCode Available	1
Detection of Fights in Videos: A Comparison Study of Anomaly Detection and Action Recognition	May 23, 2022	Action RecognitionAnomaly Detection	—Unverified	0
PAC-Wrap: Semi-Supervised PAC Anomaly Detection	May 22, 2022	Anomaly DetectionAutonomous Driving	—Unverified	0
A Subspace Method for Time Series Anomaly Detection in Cyber-Physical Systems	May 20, 2022	Anomaly DetectionTime Series	CodeCode Available	0
Anomaly Detection for Multivariate Time Series on Large-scale Fluid Handling Plant Using Two-stage Autoencoder	May 20, 2022	Anomaly DetectionTime Series	—Unverified	0
Time Series Anomaly Detection via Reinforcement Learning-Based Model Selection	May 19, 2022	Anomaly DetectionModel Selection	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	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