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 1701–1710 of 4856 papers

Title	Date	Tasks	Status
Distributed-MPC with Data-Driven Estimation of Bus Admittance Matrix in Voltage Control	Feb 28, 2022	Anomaly DetectionDistributed Optimization	—Unverified
Distributed optimization in wireless sensor networks: an island-model framework	Oct 5, 2018	Anomaly DetectionCPU	—Unverified
Anomaly Recognition from surveillance videos using 3D Convolutional Neural Networks	Jan 4, 2021	Activity RecognitionAnomaly Detection	—Unverified
Data-driven Thermal Anomaly Detection for Batteries using Unsupervised Shape Clustering	Mar 16, 2021	Anomaly DetectionClustering	—Unverified
Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection	Jan 1, 2025	Anomaly DetectionSupervised Anomaly Detection	—Unverified
ASTD Patterns for Integrated Continuous Anomaly Detection In Data Logs	Nov 10, 2024	Anomaly Detection	—Unverified
AstroM^3: A self-supervised multimodal model for astronomy	Nov 13, 2024	Anomaly DetectionAstronomy	—Unverified
Diverse Counterfactual Explanations for Anomaly Detection in Time Series	Mar 21, 2022	Anomaly Detectioncounterfactual	—Unverified
Data-Driven Semi-Supervised Machine Learning with Safety Indicators for Abnormal Driving Behavior Detection	Dec 7, 2023	Anomaly Detection	—Unverified
A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection	Jun 5, 2024	Anomaly DetectionBenchmarking	—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