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 3431–3440 of 4856 papers

Title	Date	Tasks	Status
A2Log: Attentive Augmented Log Anomaly Detection	Sep 20, 2021	Anomaly DetectionData Augmentation	—Unverified
Deep Quantile Regression for Uncertainty Estimation in Unsupervised and Supervised Lesion Detection	Sep 20, 2021	Anomaly DetectionLesion Detection	CodeCode Available
Anomaly Detection in Radar Data Using PointNets	Sep 20, 2021	Anomaly DetectionAutonomous Driving	—Unverified
A Robust and Efficient Multi-Scale Seasonal-Trend Decomposition	Sep 18, 2021	Anomaly DetectionTime Series	—Unverified
3-Dimensional Deep Learning with Spatial Erasing for Unsupervised Anomaly Segmentation in Brain MRI	Sep 14, 2021	Anomaly DetectionAnomaly Segmentation	—Unverified
Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection	Sep 13, 2021	Anomaly Detectionspeech-recognition	—Unverified
LEA-Net: Layer-wise External Attention Network for Efficient Color Anomaly Detection	Sep 12, 2021	Anomaly Detection	—Unverified
A secondary immune response based on co-evolutive populations of agents for anomaly detection and characterization	Sep 11, 2021	Anomaly DetectionSpecificity	—Unverified
Enhancing Unsupervised Anomaly Detection with Score-Guided Network	Sep 10, 2021	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
TENET: Temporal CNN with Attention for Anomaly Detection in Automotive Cyber-Physical Systems	Sep 9, 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	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