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 3771–3780 of 4856 papers

Title	Date	Tasks	Status	Hype
Label-Free Segmentation of COVID-19 Lesions in Lung CT	Sep 8, 2020	Anomaly DetectionCOVID-19 Diagnosis	—Unverified	0
Detection of Anomalies and Faults in Industrial IoT Systems by Data Mining: Study of CHRIST Osmotron Water Purification System	Sep 8, 2020	Anomaly DetectionFault Detection	—Unverified	0
Anomaly Detection With Partitioning Overfitting Autoencoder Ensembles	Sep 6, 2020	Anomaly DetectionOutlier Detection	CodeCode Available	0
Deep Learning for the Analysis of Disruption Precursors based on Plasma Tomography	Sep 6, 2020	Anomaly Detection	—Unverified	0
PySAD: A Streaming Anomaly Detection Framework in Python	Sep 5, 2020	Anomaly Detection	CodeCode Available	1
Towards the Development of Entropy-Based Anomaly Detection in an Astrophysics Simulation	Sep 5, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified	0
Simulation-Assisted Decorrelation for Resonant Anomaly Detection	Sep 4, 2020	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	0
Multivariate Time-series Anomaly Detection via Graph Attention Network	Sep 4, 2020	Anomaly DetectionGraph Attention	CodeCode Available	1
Data Anomaly Detection for Structural Health Monitoring of Bridges using Shapelet Transform	Aug 31, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified	0
Connecting Web Event Forecasting with Anomaly Detection: A Case Study on Enterprise Web Applications Using Self-Supervised Neural Networks	Aug 31, 2020	Anomaly DetectionSelf-Supervised Learning	—Unverified	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	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