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 3491–3500 of 4856 papers

Title	Date	Tasks	Status
Data-driven Thermal Anomaly Detection for Batteries using Unsupervised Shape Clustering	Mar 16, 2021	Anomaly DetectionClustering	—Unverified
Flow-based Self-supervised Density Estimation for Anomalous Sound Detection	Mar 16, 2021	Anomaly DetectionDensity Estimation	—Unverified
Unsupervised anomaly detection in digital pathology using GANs	Mar 16, 2021	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Unsupervised Anomaly Segmentation using Image-Semantic Cycle Translation	Mar 16, 2021	AnatomyAnomaly Detection	—Unverified
Distance Metric-Based Learning with Interpolated Latent Features for Location Classification in Endoscopy Image and Video	Mar 15, 2021	Anomaly DetectionFew-Shot Learning	—Unverified
Stack of discriminative autoencoders for multiclass anomaly detection in endoscopy images	Mar 15, 2021	Anomaly DetectionClustering	—Unverified
A new interpretable unsupervised anomaly detection method based on residual explanation	Mar 14, 2021	Anomaly DetectionDecision Making	—Unverified
Hyperspectral Image Denoising and Anomaly Detection Based on Low-rank and Sparse Representations	Mar 12, 2021	Anomaly DetectionDenoising	CodeCode Available
Bump Hunting in Latent Space	Mar 11, 2021	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available
Learning-Based Vulnerability Analysis of Cyber-Physical Systems	Mar 10, 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