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 2451–2460 of 4856 papers

Title	Date	Tasks	Status
Discriminative Graph-level Anomaly Detection via Dual-students-teacher Model	Aug 3, 2023	Anomaly Detection	CodeCode Available
Achieving state-of-the-art performance in the Medical Out-of-Distribution (MOOD) challenge using plausible synthetic anomalies	Aug 2, 2023	Anomaly DetectionImage Segmentation	CodeCode Available
Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach	Aug 2, 2023	Anomaly DetectionContrastive Learning	—Unverified
Semisupervised Anomaly Detection using Support Vector Regression with Quantum Kernel	Aug 1, 2023	Anomaly DetectionQuantum Machine Learning	—Unverified
PressureTransferNet: Human Attribute Guided Dynamic Ground Pressure Profile Transfer using 3D simulated Pressure Maps	Aug 1, 2023	Activity RecognitionAnomaly Detection	—Unverified
Patch-wise Auto-Encoder for Visual Anomaly Detection	Aug 1, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
A Survey of Time Series Anomaly Detection Methods in the AIOps Domain	Aug 1, 2023	Anomaly DetectionTime Series	—Unverified
A Comprehensive Study of Machine Learning Techniques for Log-Based Anomaly Detection	Jul 31, 2023	Anomaly DetectionDeep Learning	—Unverified
General Anomaly Detection of Underwater Gliders Validated by Large-scale Deployment Datasets	Jul 31, 2023	Anomaly Detection	—Unverified
Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Model	Jul 31, 2023	Anomaly DetectionExplainable artificial intelligence	—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	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