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 3361–3370 of 4856 papers

Title	Date	Tasks	Status
CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms	Nov 5, 2021	Anomaly DetectionCloud Computing	—Unverified
Towards Smart Monitored AM: Open Source in-Situ Layer-wise 3D Printing Image Anomaly Detection Using Histograms of Oriented Gradients and a Physics-Based Rendering Engine	Nov 4, 2021	Anomaly Detection	—Unverified
A Personalized Federated Learning Algorithm: an Application in Anomaly Detection	Nov 4, 2021	Anomaly DetectionFederated Learning	—Unverified
Automated, real-time hospital ICU emergency signaling: A field-level implementation	Nov 3, 2021	Anomaly Detection	—Unverified
A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methods	Nov 2, 2021	Anomaly DetectionSemi-supervised Anomaly Detection	—Unverified
A Modified Dynamic Time Warping (MDTW) Approach and Innovative Average Non-Self Match Distance (ANSD) Method for Anomaly Detection in ECG Recordings	Nov 1, 2021	Anomaly DetectionDynamic Time Warping	—Unverified
Semantic Novelty Detection in Natural Language Descriptions	Nov 1, 2021	Anomaly DetectionNovelty Detection	CodeCode Available
TADPOLE: Task ADapted Pre-Training via AnOmaLy DEtection	Nov 1, 2021	Anomaly DetectionData Augmentation	—Unverified
Markus Thill Temporal convolutional autoencoder for unsupervised anomaly detection in time series	Nov 1, 2021	Anomaly DetectionTime Series	—Unverified
Boosting Anomaly Detection Using Unsupervised Diverse Test-Time Augmentation	Oct 29, 2021	Anomaly Detection	CodeCode Available

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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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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