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 2551–2560 of 4856 papers

Title	Date	Tasks	Status
A Transfer Learning Framework for Anomaly Detection Using Model of Normality	Nov 12, 2020	Anomaly DetectionTransfer Learning	—Unverified
A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data	Jan 26, 2025	Anomaly DetectionIntrusion Detection	—Unverified
Attack-Agnostic Adversarial Detection	Jun 1, 2022	Adversarial AttackAdversarial Attack Detection	—Unverified
Attack and Anomaly Detection in IoT Sensors in IoT Sites Using Machine Learning Approaches	May 11, 2019	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Attacking Face Recognition with T-shirts: Database, Vulnerability Assessment and Detection	Nov 14, 2022	Anomaly DetectionFace Recognition	—Unverified
AttackLLM: LLM-based Attack Pattern Generation for an Industrial Control System	Apr 5, 2025	Anomaly Detection	—Unverified
Attack Rules: An Adversarial Approach to Generate Attacks for Industrial Control Systems using Machine Learning	Jul 11, 2021	Anomaly Detection	—Unverified
Attention and Autoencoder Hybrid Model for Unsupervised Online Anomaly Detection	Jan 6, 2024	Anomaly DetectionTime Series	—Unverified
Attention-Based Self-Supervised Feature Learning for Security Data	Mar 24, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Attention Boosted Autoencoder for Building Energy Anomaly Detection	Mar 28, 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