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 1631–1640 of 4856 papers

Title	Date	Tasks	Status	Hype
Synchrophasor Data Anomaly Detection on Grid Edge by 5G Communication and Adjacent Compute	Nov 13, 2023	Anomaly Detection	—Unverified	0
ADAMM: Anomaly Detection of Attributed Multi-graphs with Metadata: A Unified Neural Network Approach	Nov 13, 2023	Anomaly DetectionGraph Neural Network	CodeCode Available	0
Open-Vocabulary Video Anomaly Detection	Nov 13, 2023	Anomaly DetectionVideo Anomaly Detection	—Unverified	0
KnowSafe: Combined Knowledge and Data Driven Hazard Mitigation in Artificial Pancreas Systems	Nov 13, 2023	Anomaly Detection	—Unverified	0
CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection	Nov 12, 2023	Anomaly DetectionContrastive Learning	—Unverified	0
Dual-Branch Reconstruction Network for Industrial Anomaly Detection with RGB-D Data	Nov 12, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
DeepQC: A Deep Learning System for Automatic Quality Control of In-situ Soil Moisture Sensor Time Series Data	Nov 12, 2023	Anomaly DetectionTime Series	—Unverified	0
Open-Set Graph Anomaly Detection via Normal Structure Regularisation	Nov 12, 2023	Anomaly DetectionGraph Anomaly Detection	—Unverified	0
SCL-VI: Self-supervised Context Learning for Visual Inspection of Industrial Defects	Nov 11, 2023	Anomaly DetectionSelf-Supervised Learning	CodeCode Available	1
VT-Former: An Exploratory Study on Vehicle Trajectory Prediction for Highway Surveillance through Graph Isomorphism and Transformer	Nov 11, 2023	Anomaly DetectionAutonomous Driving	—Unverified	0

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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	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