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 3541–3550 of 4856 papers

Title	Date	Tasks	Status
Video Anomaly Detection and Explanation via Large Language Models	Jan 11, 2024	Anomaly DetectionVideo Anomaly Detection	—Unverified
Video Anomaly Detection and Localization Using Hierarchical Feature Representation and Gaussian Process Regression	Jun 1, 2015	Anomaly Detectionregression	—Unverified
Video Anomaly Detection and Localization via Gaussian Mixture Fully Convolutional Variational Autoencoder	May 29, 2018	Anomaly DetectionDecoder	—Unverified
Video Anomaly Detection By The Duality Of Normality-Granted Optical Flow	May 10, 2021	Anomaly DetectionOptical Flow Estimation	—Unverified
Video Anomaly Detection for Smart Surveillance	Apr 1, 2020	Anomaly DetectionTemporal Localization	—Unverified
Video Anomaly Detection in 10 Years: A Survey and Outlook	May 29, 2024	Anomaly DetectionSurvey	—Unverified
Video Anomaly Detection using GAN	Nov 23, 2023	Anomaly DetectionEvent Detection	—Unverified
Video Anomaly Detection Using Pre-Trained Deep Convolutional Neural Nets and Context Mining	Oct 6, 2020	Anomaly DetectionDenoising	—Unverified
Video Anomaly Detection via Prediction Network with Enhanced Spatio-Temporal Memory Exchange	Jun 26, 2022	Anomaly DetectionDecoder	—Unverified
Video Anomaly Detection via Sequentially Learning Multiple Pretext Tasks	Jan 1, 2023	Anomaly DetectionVideo Anomaly 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	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