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 3031–3040 of 4856 papers

Title	Date	Tasks	Status
A Benchmark dataset for predictive maintenance	Jul 12, 2022	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Stochastic Functional Analysis and Multilevel Vector Field Anomaly Detection	Jul 11, 2022	Anomaly Detection	—Unverified
On the Robustness and Anomaly Detection of Sparse Neural Networks	Jul 9, 2022	Anomaly Detection	—Unverified
Generative Adversarial Networks and Other Generative Models	Jul 8, 2022	Anomaly DetectionImage Generation	—Unverified
Signed Network Embedding with Application to Simultaneous Detection of Communities and Anomalies	Jul 8, 2022	Anomaly DetectionCommunity Detection	—Unverified
ENCODE: Encoding NetFlows for Network Anomaly Detection	Jul 8, 2022	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available
GCN-based Multi-task Representation Learning for Anomaly Detection in Attributed Networks	Jul 8, 2022	Anomaly DetectionCommunity Detection	—Unverified
Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems	Jul 8, 2022	Active LearningAnomaly Detection	—Unverified
Red PANDA: Disambiguating Anomaly Detection by Removing Nuisance Factors	Jul 7, 2022	Anomaly DetectionAttribute	—Unverified
Leveraging Log Instructions in Log-based Anomaly Detection	Jul 7, 2022	Anomaly DetectionCPU	CodeCode Available

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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