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 1121–1130 of 4856 papers

Title	Date	Tasks	Status
Attack and Anomaly Detection in IoT Sensors in IoT Sites Using Machine Learning Approaches	May 11, 2019	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Anomaly Detection and Localization based on Double Kernelized Scoring and Matrix Kernels	Dec 15, 2020	Anomaly DetectionTime Series Analysis	—Unverified
AEGR: A simple approach to gradient reversal in autoencoders for network anomaly detection	Dec 21, 2019	Anomaly DetectionData Augmentation	—Unverified
Attack-Agnostic Adversarial Detection	Jun 1, 2022	Adversarial AttackAdversarial Attack Detection	—Unverified
A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data	Jan 26, 2025	Anomaly DetectionIntrusion Detection	—Unverified
Anomaly Detection and Localisation using Mixed Graphical Models	Jul 20, 2016	Anomaly Detection	—Unverified
Active Anomaly Detection with Switching Cost	Oct 28, 2018	Anomaly Detection	—Unverified
Continual learning on deployment pipelines for Machine Learning Systems	Dec 5, 2022	Anomaly DetectionContinual Learning	—Unverified
A Transfer Learning Framework for Anomaly Detection Using Model of Normality	Nov 12, 2020	Anomaly DetectionTransfer Learning	—Unverified
Atom dimension adaptation for infinite set dictionary learning	Sep 10, 2024	Anomaly DetectionDictionary Learning	—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