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 4411–4420 of 4856 papers

Title	Date	Tasks	Status
Graph Embedded Pose Clustering for Anomaly Detection	Dec 26, 2019	Anomaly DetectionClustering	CodeCode Available
Multivariate Time Series Anomaly Detection using DiffGAN Model	Jan 3, 2025	Anomaly DetectionGenerative Adversarial Network	CodeCode Available
Label-based Graph Augmentation with Metapath for Graph Anomaly Detection	Aug 21, 2023	Anomaly DetectionGraph Anomaly Detection	CodeCode Available
TeVAE: A Variational Autoencoder Approach for Discrete Online Anomaly Detection in Variable-state Multivariate Time-series Data	Jul 9, 2024	Anomaly DetectionTime Series	CodeCode Available
BINet: Multi-perspective Business Process Anomaly Classification	Feb 8, 2019	Anomaly ClassificationAnomaly Detection	CodeCode Available
SAIFE: Unsupervised Wireless Spectrum Anomaly Detection with Interpretable Features	Jul 22, 2018	Anomaly DetectionData Compression	CodeCode Available
Multi-view Deep One-class Classification: A Systematic Exploration	Apr 27, 2021	Anomaly DetectionClassification	CodeCode Available
ADKGD: Anomaly Detection in Knowledge Graphs with Dual-Channel Training	Jan 13, 2025	Anomaly DetectionKnowledge Graphs	CodeCode Available
VegaEdge: Edge AI Confluence Anomaly Detection for Real-Time Highway IoT-Applications	Nov 14, 2023	Anomaly DetectionTrajectory Prediction	CodeCode Available
Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection	Mar 18, 2019	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available

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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