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 3651–3660 of 4856 papers

Title	Date	Tasks	Status
OLED: One-Class Learned Encoder-Decoder Network with Adversarial Context Masking for Novelty Detection	Mar 27, 2021	Anomaly DetectionDecoder	CodeCode Available
Deep-RBF Networks for Anomaly Detection in Automotive Cyber-Physical Systems	Mar 25, 2021	Anomaly Detection	—Unverified
Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation	Mar 25, 2021	Anomaly DetectionDensity Estimation	—Unverified
Learning Stable Representations with Full Encoder	Mar 25, 2021	Anomaly DetectionData Compression	—Unverified
Including Sparse Production Knowledge into Variational Autoencoders to Increase Anomaly Detection Reliability	Mar 24, 2021	Anomaly DetectionTime Series	—Unverified
Non-Compression Auto-Encoder for Detecting Road Surface Abnormality via Vehicle Driving Noise	Mar 24, 2021	Anomaly DetectionTime Series	CodeCode Available
3D Reasoning for Unsupervised Anomaly Detection in Pediatric WbMRI	Mar 24, 2021	Anomaly DetectionLesion Detection	—Unverified
Anomaly Detection using Principles of Human Perception	Mar 23, 2021	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
Unsupervised Two-Stage Anomaly Detection	Mar 22, 2021	Anomaly DetectionImage Reconstruction	—Unverified
A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients	Mar 22, 2021	Anomaly DetectionDeep Learning	CodeCode Available

Show:10 25 50

← PrevPage 366 of 486Next →

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