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 2671–2680 of 4856 papers

Title	Date	Tasks	Status
Hallucinated Heartbeats: Anomaly-Aware Remote Pulse Estimation	Mar 11, 2023	Anomaly DetectionFace Presentation Attack Detection	CodeCode Available
Deep Anomaly Detection on Tennessee Eastman Process Data	Mar 10, 2023	Anomaly DetectionChemical Process	—Unverified
Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detection	Mar 10, 2023	Anomaly Detection	—Unverified
Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution Detection	Mar 10, 2023	Anomaly DetectionImage Captioning	CodeCode Available
3D Masked Autoencoders with Application to Anomaly Detection in Non-Contrast Enhanced Breast MRI	Mar 10, 2023	Anomaly DetectionLesion Detection	—Unverified
Automated visual inspection of CMS HGCAL silicon sensor surface using an ensemble of a deep convolutional autoencoder and classifier	Mar 9, 2023	Anomaly Detection	—Unverified
Updated version: A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency	Mar 9, 2023	Anomaly DetectionVideo Anomaly Detection	—Unverified
Synthetic Pseudo Anomalies for Unsupervised Video Anomaly Detection: A Simple yet Efficient Framework based on Masked Autoencoder	Mar 9, 2023	Anomaly DetectionOne-Class Classification	—Unverified
Multi-level Memory-augmented Appearance-Motion Correspondence Framework for Video Anomaly Detection	Mar 9, 2023	Anomaly DetectionVideo Anomaly Detection	—Unverified
Learning Representation for Anomaly Detection of Vehicle Trajectories	Mar 9, 2023	Anomaly DetectionAutonomous Driving	—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