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 3871–3880 of 4856 papers

Title	Date	Tasks	Status	Hype
Leveraging Siamese Networks for One-Shot Intrusion Detection Model	Jun 27, 2020	Anomaly DetectionIntrusion Detection	—Unverified	0
Efficient Deep CNN-BiLSTM Model for Network Intrusion Detection	Jun 26, 2020	Anomaly DetectionIntrusion Detection	CodeCode Available	1
An Investigation of Traffic Density Changes inside Wuhan during the COVID-19 Epidemic with GF-2 Time-Series Images	Jun 26, 2020	Anomaly DetectionTime Series	—Unverified	0
Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy	Jun 26, 2020	Anomaly Detection	CodeCode Available	1
Anomaly Detection using Deep Reconstruction and Forecasting for Autonomous Systems	Jun 25, 2020	Anomaly Detection	—Unverified	0
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types	Jun 24, 2020	Anomaly DetectionKnowledge Graphs	—Unverified	0
Traffic congestion anomaly detection and prediction using deep learning	Jun 23, 2020	Anomaly DetectionDeep Learning	—Unverified	0
Fixing Inventory Inaccuracies At Scale	Jun 23, 2020	Anomaly DetectionMatrix Completion	—Unverified	0
Anomaly Detection in Medical Imaging with Deep Perceptual Autoencoders	Jun 23, 2020	Anomaly DetectionMedical Image Analysis	CodeCode Available	1
Locally Masked Convolution for Autoregressive Models	Jun 22, 2020	Anomaly DetectionDensity Estimation	CodeCode Available	1

Show:10 25 50

← PrevPage 388 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