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 3711–3720 of 4856 papers

Title	Date	Tasks	Status
Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder	Feb 2, 2021	Anomaly DetectionDensity Estimation	—Unverified
AURSAD: Universal Robot Screwdriving Anomaly Detection Dataset	Feb 2, 2021	Anomaly Detection	CodeCode Available
Decentralized Federated Learning Preserves Model and Data Privacy	Feb 1, 2021	Anomaly DetectionFederated Learning	—Unverified
Robust Attack Detection Approach for IIoT Using Ensemble Classifier	Jan 30, 2021	Anomaly DetectionIntrusion Detection	—Unverified
The Deep Radial Basis Function Data Descriptor (D-RBFDD) Network: A One-Class Neural Network for Anomaly Detection	Jan 29, 2021	Anomaly Detectionimage-classification	—Unverified
Autoencoder-based Condition Monitoring and Anomaly Detection Method for Rotating Machines	Jan 27, 2021	Anomaly Detection	—Unverified
Wisdom of the Contexts: Active Ensemble Learning for Contextual Anomaly Detection	Jan 27, 2021	Active LearningAnomaly Detection	—Unverified
Blind Image Denoising and Inpainting Using Robust Hadamard Autoencoders	Jan 26, 2021	Anomaly DetectionDenoising	CodeCode Available
Leveraging 3D Information in Unsupervised Brain MRI Segmentation	Jan 26, 2021	Anomaly DetectionMRI segmentation	—Unverified
Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey	Jan 25, 2021	Anomaly DetectionBIG-bench Machine Learning	—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	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