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 1041–1050 of 4856 papers

Title	Date	Tasks	Status
PIF: Anomaly detection via preference embedding	May 15, 2025	Anomaly Detection	—Unverified
A Representation Learning Approach to Feature Drift Detection in Wireless Networks	May 15, 2025	Anomaly DetectionDrift Detection	—Unverified
Cybersecurity threat detection based on a UEBA framework using Deep Autoencoders	May 14, 2025	Anomaly Detection	—Unverified
WSCIF: A Weakly-Supervised Color Intelligence Framework for Tactical Anomaly Detection in Surveillance Keyframes	May 14, 2025	Anomaly Detection	—Unverified
Deep Probabilistic Modeling of User Behavior for Anomaly Detection via Mixture Density Networks	May 13, 2025	Anomaly Detection	—Unverified
Structural-Temporal Coupling Anomaly Detection with Dynamic Graph Transformer	May 13, 2025	Anomaly DetectionEpidemiology	CodeCode Available
Crowd Scene Analysis using Deep Learning Techniques	May 13, 2025	Anomaly DetectionBinary Classification	—Unverified
Fault Detection Method for Power Conversion Circuits Using Thermal Image and Convolutional Autoencoder	May 13, 2025	Anomaly DetectionFault Detection	—Unverified
Isolation Forest in Novelty Detection Scenario	May 13, 2025	Anomaly DetectionNovelty Detection	—Unverified
neuralGAM: An R Package for Fitting Generalized Additive Neural Networks	May 13, 2025	Additive modelsAnomaly Detection	—Unverified

Show:10 25 50

← PrevPage 105 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	HETMM	Detection AUROC	99.8	—	Unverified
7	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	PBAS	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	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	DDAD	Detection AUROC	98.9	—	Unverified
6	Dinomaly ViT-L (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