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 1661–1670 of 4856 papers

Title	Date	Tasks	Status	Hype
Respiratory Anomaly Detection using Reflected Infrared Light-wave Signals	Nov 2, 2023	Anomaly Detection	—Unverified	0
Time Series Anomaly Detection using Diffusion-based Models	Nov 2, 2023	Anomaly DetectionTime Series	CodeCode Available	1
Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulation	Nov 2, 2023	Anomaly DetectionDepth Anomaly Detection and Segmentation	CodeCode Available	1
Few-shot time-series anomaly detection with unsupervised domain adaptation	Nov 1, 2023	Anomaly DetectionDomain Adaptation	—Unverified	0
Architecture of Data Anomaly Detection-Enhanced Decentralized Expert System for Early-Stage Alzheimer's Disease Prediction	Nov 1, 2023	Anomaly DetectionDiagnostic	—Unverified	0
Model-driven Engineering for Machine Learning Components: A Systematic Literature Review	Nov 1, 2023	Anomaly DetectionDecision Making	—Unverified	0
CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection	Nov 1, 2023	Anomaly DetectionLanguage Modeling	—Unverified	0
Log-based Anomaly Detection of Enterprise Software: An Empirical Study	Oct 31, 2023	Anomaly Detection	—Unverified	0
A Low-cost Strategic Monitoring Approach for Scalable and Interpretable Error Detection in Deep Neural Networks	Oct 31, 2023	Anomaly Detection	—Unverified	0
Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental Learning	Oct 30, 2023	Anomaly Detectionclass-incremental learning	CodeCode Available	0

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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