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 2991–3000 of 4856 papers

Title	Date	Tasks	Status
Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging	Jan 18, 2022	Anomaly DetectionInductive Bias	—Unverified
Real-Time Anomaly Detection for Multivariate Data Stream	Jan 17, 2022	Anomaly Detection	—Unverified
FCDD - Explainable Anomaly Detection	Jan 17, 2022	Anomaly Detection	—Unverified
Self-Supervised Anomaly Detection by Self-Distillation and Negative Sampling	Jan 17, 2022	Anomaly DetectionOut of Distribution (OOD) Detection	CodeCode Available
Adversarial Machine Learning Threat Analysis and Remediation in Open Radio Access Network (O-RAN)	Jan 16, 2022	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Concise Logarithmic Loss Function for Robust Training of Anomaly Detection Model	Jan 15, 2022	Anomaly Detection	—Unverified
Forecast-based Multi-aspect Framework for Multivariate Time-series Anomaly Detection	Jan 13, 2022	Anomaly DetectionTime Series	—Unverified
Functional Anomaly Detection: a Benchmark Study	Jan 13, 2022	Anomaly DetectionDescriptive	—Unverified
Local2Global: A distributed approach for scaling representation learning on graphs	Jan 12, 2022	Anomaly DetectionGraph Representation Learning	CodeCode Available
Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation	Jan 11, 2022	Active LearningAnomaly Detection	—Unverified

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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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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