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 2671–2680 of 4856 papers

Title	Date	Tasks	Status	Hype
Registration based Few-Shot Anomaly Detection	Jul 15, 2022	Anomaly Detection	CodeCode Available	2
Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networks	Jul 14, 2022	Anomaly DetectionIntrusion Detection	CodeCode Available	1
Experiments on Anomaly Detection in Autonomous Driving by Forward-Backward Style Transfers	Jul 13, 2022	Anomaly DetectionAutonomous Driving	—Unverified	0
Data-Driven Thermal Modelling for Anomaly Detection in Electric Vehicle Charging Stations	Jul 12, 2022	Anomaly Detection	—Unverified	0
A Benchmark dataset for predictive maintenance	Jul 12, 2022	Anomaly DetectionBIG-bench Machine Learning	—Unverified	0
Stochastic Functional Analysis and Multilevel Vector Field Anomaly Detection	Jul 11, 2022	Anomaly Detection	—Unverified	0
On the Robustness and Anomaly Detection of Sparse Neural Networks	Jul 9, 2022	Anomaly Detection	—Unverified	0
Signed Network Embedding with Application to Simultaneous Detection of Communities and Anomalies	Jul 8, 2022	Anomaly DetectionCommunity Detection	—Unverified	0
Generative Adversarial Networks and Other Generative Models	Jul 8, 2022	Anomaly DetectionImage Generation	—Unverified	0
GCN-based Multi-task Representation Learning for Anomaly Detection in Attributed Networks	Jul 8, 2022	Anomaly DetectionCommunity 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	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