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 3951–3960 of 4856 papers

Title	Date	Tasks	Status
Deep Learning Models for Visual Inspection on Automotive Assembling Line	Jul 2, 2020	Anomaly DetectionDeep Learning	—Unverified
Feature Extraction for Novelty Detection in Network Traffic	Jun 30, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Abnormal activity capture from passenger flow of elevator based on unsupervised learning and fine-grained multi-label recognition	Jun 29, 2020	Anomaly DetectionClustering	—Unverified
Generative Damage Learning for Concrete Aging Detection using Auto-flight Images	Jun 27, 2020	Anomaly DetectionImage-to-Image Translation	—Unverified
Leveraging Siamese Networks for One-Shot Intrusion Detection Model	Jun 27, 2020	Anomaly DetectionIntrusion Detection	—Unverified
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
Anomaly Detection using Deep Reconstruction and Forecasting for Autonomous Systems	Jun 25, 2020	Anomaly Detection	—Unverified
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types	Jun 24, 2020	Anomaly DetectionKnowledge Graphs	—Unverified
Fixing Inventory Inaccuracies At Scale	Jun 23, 2020	Anomaly DetectionMatrix Completion	—Unverified
Traffic congestion anomaly detection and prediction using deep learning	Jun 23, 2020	Anomaly DetectionDeep Learning	—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