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 71–80 of 4856 papers

Title	Date	Tasks	Status	Hype
Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC	May 30, 2025	Anomaly DetectionQuantum Machine Learning	CodeCode Available	0
Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation	May 30, 2025	3D Anomaly DetectionAnomaly Detection	CodeCode Available	0
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded Devices	May 30, 2025	Anomaly Detection	CodeCode Available	0
Anomaly Detection and Improvement of Clusters using Enhanced K-Means Algorithm	May 30, 2025	Anomaly Detection	—Unverified	0
HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection	May 30, 2025	Anomaly DetectionComputational Efficiency	—Unverified	0
SentinelAgent: Graph-based Anomaly Detection in Multi-Agent Systems	May 30, 2025	Anomaly DetectionLarge Language Model	—Unverified	0
Machine Learning-Based Anomaly Detection of Correlated Sensor Data: An Integrated Principal Component Analysis-Autoencoder Approach	May 29, 2025	Anomaly DetectionHybrid Machine Learning	—Unverified	0
VAU-R1: Advancing Video Anomaly Understanding via Reinforcement Fine-Tuning	May 29, 2025	Anomaly DetectionDescriptive	CodeCode Available	2
Sentinel: Scheduling Live Streams with Proactive Anomaly Detection in Crowdsourced Cloud-Edge Platforms	May 29, 2025	Anomaly DetectionScheduling	—Unverified	0
FreRA: A Frequency-Refined Augmentation for Contrastive Learning on Time Series Classification	May 29, 2025	Anomaly DetectionContrastive Learning	CodeCode Available	1

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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