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 4131–4140 of 4856 papers

Title	Date	Tasks	Status
Real-time Anomaly Detection and Classification in Streaming PMU Data	Nov 14, 2019	Anomaly DetectionClassification	—Unverified
Enabling Efficient Privacy-Assured Outlier Detection over Encrypted Incremental Datasets	Nov 14, 2019	Anomaly DetectionOutlier Detection	—Unverified
Real-Time Anomaly Detection for Advanced Manufacturing: Improving on Twitter's State of the Art	Nov 13, 2019	Anomaly DetectionTime Series	—Unverified
Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things	Nov 13, 2019	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Anomaly Detection in Large Scale Networks with Latent Space Models	Nov 13, 2019	Anomaly Detection	—Unverified
Uncertainty on Asynchronous Time Event Prediction	Nov 13, 2019	Anomaly DetectionPrediction	CodeCode Available
Real-time ultra-low power ECG anomaly detection using an event-driven neuromorphic processor	Nov 13, 2019	Anomaly Detection	—Unverified
Deep Generative Models Strike Back! Improving Understanding and Evaluation in Light of Unmet Expectations for OoD Data	Nov 12, 2019	Anomaly DetectionDimensionality Reduction	—Unverified
Anomaly Detection for Industrial Control Systems Using Sequence-to-Sequence Neural Networks	Nov 12, 2019	Anomaly DetectionTime Series	CodeCode Available
Deep Variational Semi-Supervised Novelty Detection	Nov 12, 2019	Anomaly DetectionAstronomy	—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