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 3681–3690 of 4856 papers

Title	Date	Tasks	Status
Neural Network-based Quantization for Network Automation	Mar 4, 2021	Anomaly DetectionDeep Learning	—Unverified
Helicopter Track Identification with Autoencoder	Mar 3, 2021	Anomaly DetectionSelf-Supervised Learning	—Unverified
Image/Video Deep Anomaly Detection: A Survey	Mar 2, 2021	Anomaly DetectionEvent Detection	—Unverified
AdeNet: Deep learning architecture that identifies damaged electrical insulators in power lines	Mar 2, 2021	Anomaly DetectionTransfer Learning	CodeCode Available
Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection	Mar 1, 2021	Anomaly DetectionFew-Shot Learning	—Unverified
Online anomaly detection using statistical leverage for streaming business process events	Mar 1, 2021	Anomaly Detection	CodeCode Available
Automatic Feature Extraction for Heartbeat Anomaly Detection	Feb 24, 2021	Anomaly DetectionDecoder	CodeCode Available
Railway Anomaly detection model using synthetic defect images generated by CycleGAN	Feb 24, 2021	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Deep Compact Polyhedral Conic Classifier for Open and Closed Set Recognition	Feb 24, 2021	Anomaly DetectionGeneral Classification	—Unverified
Sleep Apnea and Respiratory Anomaly Detection from a Wearable Band and Oxygen Saturation	Feb 24, 2021	Anomaly Detection	—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