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 2561–2570 of 4856 papers

Title	Date	Tasks	Status	Hype
Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation	Sep 21, 2022	Anomaly DetectionData Augmentation	CodeCode Available	1
Learning Acceptance Regions for Many Classes with Anomaly Detection	Sep 20, 2022	Anomaly DetectionClassification	—Unverified	0
An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots	Sep 20, 2022	Anomaly Detection	CodeCode Available	1
Collaborative Anomaly Detection	Sep 20, 2022	Anomaly DetectionDensity Estimation	—Unverified	0
Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model	Sep 19, 2022	Anomaly DetectionDensity Ratio Estimation	—Unverified	0
Infrared: A Meta Bug Detector	Sep 18, 2022	Anomaly Detection	—Unverified	0
LogGD:Detecting Anomalies from System Logs by Graph Neural Networks	Sep 16, 2022	Anomaly Detection	—Unverified	0
An Attention-guided Multistream Feature Fusion Network for Localization of Risky Objects in Driving Videos	Sep 16, 2022	Anomaly DetectionObject	CodeCode Available	1
Anomaly Detection in Automatic Generation Control Systems Based on Traffic Pattern Analysis and Deep Transfer Learning	Sep 16, 2022	Anomaly DetectionTransfer Learning	—Unverified	0
IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective	Sep 16, 2022	Anomaly DetectionAutoML	CodeCode Available	2

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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