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 3521–3530 of 4856 papers

Title	Date	Tasks	Status	Hype
Sleep Apnea and Respiratory Anomaly Detection from a Wearable Band and Oxygen Saturation	Feb 24, 2021	Anomaly Detection	—Unverified	0
Deep Compact Polyhedral Conic Classifier for Open and Closed Set Recognition	Feb 24, 2021	Anomaly DetectionGeneral Classification	—Unverified	0
Automatic Feature Extraction for Heartbeat Anomaly Detection	Feb 24, 2021	Anomaly DetectionDecoder	CodeCode Available	0
Railway Anomaly detection model using synthetic defect images generated by CycleGAN	Feb 24, 2021	Anomaly DetectionBIG-bench Machine Learning	—Unverified	0
Neuroscience-Inspired Algorithms for the Predictive Maintenance of Manufacturing Systems	Feb 23, 2021	Anomaly Detection	—Unverified	0
An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery	Feb 23, 2021	Anomaly DetectionBIG-bench Machine Learning	—Unverified	0
SliceNStitch: Continuous CP Decomposition of Sparse Tensor Streams	Feb 23, 2021	Anomaly DetectionRecommendation Systems	CodeCode Available	1
Unsupervised Brain Anomaly Detection and Segmentation with Transformers	Feb 23, 2021	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
Robust and Transferable Anomaly Detection in Log Data using Pre-Trained Language Models	Feb 23, 2021	Anomaly Detection	—Unverified	0
Few-shot Network Anomaly Detection via Cross-network Meta-learning	Feb 22, 2021	Anomaly DetectionFew-Shot Learning	CodeCode Available	1

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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