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 3971–3980 of 4856 papers

Title	Date	Tasks	Status	Hype
Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm	Apr 16, 2020	Anomaly DetectionNovelty Detection	CodeCode Available	1
Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning	Apr 16, 2020	Anomaly DetectionGeneral Classification	CodeCode Available	0
Continual Learning for Anomaly Detection in Surveillance Videos	Apr 15, 2020	Anomaly DetectionAnomaly Detection In Surveillance Videos	—Unverified	0
Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing	Apr 15, 2020	Anomaly DetectionEdge-computing	—Unverified	0
Anomaly Detection in Trajectory Data with Normalizing Flows	Apr 13, 2020	Anomaly DetectionDensity Estimation	—Unverified	0
Hybrid Attention Networks for Flow and Pressure Forecasting in Water Distribution Systems	Apr 13, 2020	Anomaly DetectionDecision Making	—Unverified	0
A Survey of Single-Scene Video Anomaly Detection	Apr 13, 2020	Anomaly DetectionSurvey	—Unverified	0
Towards Anomaly Detection in Dashcam Videos	Apr 11, 2020	Anomaly DetectionOne-Class Classification	—Unverified	0
Anomaly Detection for Time Series Using VAE-LSTM Hybrid Model	Apr 9, 2020	Anomaly DetectionTime Series	CodeCode Available	1
Anomaly Detection with SDAE	Apr 9, 2020	Anomaly Detection	—Unverified	0

Show:10 25 50

← PrevPage 398 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	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