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 4121–4130 of 4856 papers

Title	Date	Tasks	Status
A Promotion Method for Generation Error Based Video Anomaly Detection	Nov 19, 2019	Anomaly DetectionVideo Anomaly Detection	—Unverified
On the Impact of Object and Sub-component Level Segmentation Strategies for Supervised Anomaly Detection within X-ray Security Imagery	Nov 19, 2019	Anomaly DetectionSegmentation	—Unverified
Attention Guided Anomaly Localization in Images	Nov 19, 2019	Anomaly DetectionAnomaly Localization	—Unverified
Seq2Seq RNN based Gait Anomaly Detection from Smartphone Acquired Multimodal Motion Data	Nov 19, 2019	Anomaly Detection	CodeCode Available
Deep Anomaly Detection with Deviation Networks	Nov 19, 2019	Anomaly DetectionCyber Attack Detection	CodeCode Available
Online Adaptive Asymmetric Active Learning with Limited Budgets	Nov 18, 2019	Active LearningAnomaly Detection	CodeCode Available
Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models	Nov 18, 2019	Anomaly DetectionAutonomous Driving	—Unverified
RSM-GAN: A Convolutional Recurrent GAN for Anomaly Detection in Contaminated Seasonal Multivariate Time Series	Nov 16, 2019	Anomaly DetectionCausal Inference	—Unverified
Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy	Nov 16, 2019	Anomaly DetectionBackdoor Attack	—Unverified
Hybrid data-driven physics model-based framework for enhance cyber-physical smart grid security	Nov 15, 2019	Anomaly DetectionState Estimation	—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	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