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 4351–4360 of 4856 papers

Title	Date	Tasks	Status	Hype
Segmentation-Based Deep-Learning Approach for Surface-Defect Detection	Mar 20, 2019	Anomaly DetectionDeep Learning	CodeCode Available	1
Ensemble Clustering for Graphs: Comparisons and Applications	Mar 19, 2019	Anomaly DetectionClustering	CodeCode Available	0
Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection	Mar 18, 2019	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available	0
Learning Competitive and Discriminative Reconstructions for Anomaly Detection	Mar 17, 2019	Anomaly Detection	—Unverified	0
Multi-Stage Fault Warning for Large Electric Grids Using Anomaly Detection and Machine Learning	Mar 15, 2019	Anomaly DetectionBIG-bench Machine Learning	—Unverified	0
GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection	Mar 15, 2019	Anomaly Detection	CodeCode Available	0
ADS-ME: Anomaly Detection System for Micro-expression Spotting	Mar 11, 2019	Anomaly DetectionMicro-Expression Spotting	—Unverified	0
Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos	Mar 8, 2019	Anomaly DetectionDecoder	CodeCode Available	0
Improving SIEM for Critical SCADA Water Infrastructures Using Machine Learning	Mar 6, 2019	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	0
Explaining Anomalies Detected by Autoencoders Using SHAP	Mar 6, 2019	Anomaly DetectionOutlier Detection	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