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 2241–2250 of 4856 papers

Title	Date	Tasks	Status
Composite Score for Anomaly Detection in Imbalanced Real-World Industrial Dataset	Nov 25, 2022	Anomaly DetectionGenerative Adversarial Network	—Unverified
Anomaly Detection: Review and preliminary Entropy method tests	Aug 29, 2017	Anomaly Detection	—Unverified
Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection	Jan 9, 2018	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Composite Kernel Local Angular Discriminant Analysis for Multi-Sensor Geospatial Image Analysis	Jul 18, 2016	Anomaly Detection	—Unverified
A Mallows-like Criterion for Anomaly Detection with Random Forest Implementation	May 29, 2024	Anomaly Detection	—Unverified
Component-aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detection	Feb 17, 2025	Anomaly Detection	—Unverified
Anomaly detection optimization using big data and deep learning to reduce false-positive	Sep 28, 2022	Anomaly DetectionIntrusion Detection	—Unverified
ADDAI: Anomaly Detection using Distributed AI	May 2, 2022	Anomaly DetectionDistributed Computing	—Unverified
Group Anomaly Detection using Flexible Genre Models	Dec 1, 2011	Anomaly DetectionGroup Anomaly Detection	—Unverified
Complying with the EU AI Act: Innovations in Explainable and User-Centric Hand Gesture Recognition	Feb 4, 2025	Anomaly DetectionGesture Recognition	—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