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
Applications of Signature Methods to Market Anomaly Detection	Jan 7, 2022	Anomaly DetectionTime Series	—Unverified
Applied Bayesian Structural Health Monitoring: inclinometer data anomaly detection and forecasting	Jul 1, 2023	Anomaly DetectionDecision Making	—Unverified
Applied Machine Learning to Anomaly Detection in Enterprise Purchase Processes	May 23, 2024	Anomaly Detection	—Unverified
Intelligent Approaches to Predictive Analytics in Occupational Health and Safety in India	Dec 20, 2024	Anomaly DetectionDecision Making	—Unverified
Applying Quantum Autoencoders for Time Series Anomaly Detection	Oct 5, 2024	Anomaly DetectionFraud Detection	—Unverified
Approaching adverse event detection utilizing transformers on clinical time-series	Nov 15, 2023	Anomaly DetectionEvent Detection	—Unverified
Approximate Maximum Halfspace Discrepancy	Jun 25, 2021	Anomaly Detection	—Unverified
Approximating DTW with a convolutional neural network on EEG data	Jan 30, 2023	Anomaly DetectionComputational Efficiency	—Unverified
Towards Anomaly-Aware Pre-Training and Fine-Tuning for Graph Anomaly Detection	Apr 19, 2025	Anomaly DetectionGraph Anomaly Detection	—Unverified
A principled distributional approach to trajectory similarity measurement	Jan 1, 2023	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	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