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 3641–3650 of 4856 papers

Title	Date	Tasks	Status
Locally Adaptive One-Class Classifier Fusion with Dynamic p-Norm Constraints for Robust Anomaly Detection	Nov 10, 2024	Anomaly DetectionComputational Efficiency	—Unverified
Locally Differentially Private Embedding Models in Distributed Fraud Prevention Systems	Jan 3, 2024	Anomaly DetectionPrivacy Preserving	—Unverified
Vision-Language Models Assisted Unsupervised Video Anomaly Detection	Sep 21, 2024	Anomaly DetectionContrastive Learning	—Unverified
Developing an Unsupervised Real-time Anomaly Detection Scheme for Time Series with Multi-seasonality	Aug 3, 2019	Anomaly DetectionLine Detection	—Unverified
LogAnMeta: Log Anomaly Detection Using Meta Learning	Dec 21, 2022	Anomaly DetectionMeta-Learning	—Unverified
Log-based Anomaly Detection of Enterprise Software: An Empirical Study	Oct 31, 2023	Anomaly Detection	—Unverified
LogELECTRA: Self-supervised Anomaly Detection for Unstructured Logs	Feb 16, 2024	Anomaly DetectionSelf-Supervised Anomaly Detection	—Unverified
LogGD:Detecting Anomalies from System Logs by Graph Neural Networks	Sep 16, 2022	Anomaly Detection	—Unverified
LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection	Sep 3, 2023	Anomaly Detection	—Unverified
LogicAD: Explainable Anomaly Detection via VLM-based Text Feature Extraction	Jan 3, 2025	Anomaly DetectionVisual Reasoning	—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