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 3801–3810 of 4856 papers

Title	Date	Tasks	Status
Multimodal video analysis for crowd anomaly detection using open access tourism cameras	May 21, 2024	Anomaly DetectionDecision Making	—Unverified
Multi-Perspective Anomaly Detection	May 20, 2021	Anomaly DetectionDenoising	—Unverified
Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection	Jul 24, 2021	Anomaly DetectionFeature Engineering	—Unverified
Multiple Abnormality Detection for Automatic Medical Image Diagnosis Using Bifurcated Convolutional Neural Network	Sep 16, 2018	Anomaly DetectionGeneral Classification	—Unverified
Multiple Inputs Neural Networks for Medicare fraud Detection	Mar 11, 2022	Anomaly DetectionFraud Detection	—Unverified
Multiple-Input Variational Auto-Encoder for Anomaly Detection in Heterogeneous Data	Jan 14, 2025	Anomaly Detection	—Unverified
Multiple Instance Learning for Detecting Anomalies over Sequential Real-World Datasets	Oct 4, 2022	Anomaly DetectionMultiple Instance Learning	—Unverified
Multiple profiles sensor-based monitoring and anomaly detection	Oct 31, 2018	Anomaly Detection	—Unverified
MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast	Oct 21, 2024	Anomaly DetectionContrastive Learning	—Unverified
Multiresolution Feature Guidance Based Transformer for Anomaly Detection	May 24, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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