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 2821–2830 of 4856 papers

Title	Date	Tasks	Status	Hype
Feature anomaly detection system (FADS) for intelligent manufacturing	Apr 21, 2022	Anomaly Detection	—Unverified	0
A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection Algorithms	Apr 21, 2022	Anomaly DetectionFraud Detection	CodeCode Available	1
Representation Learning for Content-Sensitive Anomaly Detection in Industrial Networks	Apr 20, 2022	Anomaly DetectionIntrusion Detection	CodeCode Available	1
Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations	Apr 20, 2022	Anomaly DetectionDimensionality Reduction	—Unverified	0
Multi-Component Optimization and Efficient Deployment of Neural-Networks on Resource-Constrained IoT Hardware	Apr 20, 2022	Anomaly DetectionModel Optimization	CodeCode Available	1
Scalable Motif Counting for Large-scale Temporal Graphs	Apr 20, 2022	Anomaly DetectionCPU	CodeCode Available	0
Robustness Testing of Data and Knowledge Driven Anomaly Detection in Cyber-Physical Systems	Apr 20, 2022	Anomaly Detectionimage-classification	—Unverified	0
Sintel: A Machine Learning Framework to Extract Insights from Signals	Apr 19, 2022	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	3
"Flux+Mutability": A Conditional Generative Approach to One-Class Classification and Anomaly Detection	Apr 19, 2022	Anomaly DetectionMulti-class Classification	—Unverified	0
Multi-scale Anomaly Detection for Big Time Series of Industrial Sensors	Apr 18, 2022	Anomaly DetectionTime Series	—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	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