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 2931–2940 of 4856 papers

Title	Date	Tasks	Status
Self-Supervised Guided Segmentation Framework for Unsupervised Anomaly Detection	Sep 26, 2022	Anomaly DetectionSegmentation	—Unverified
Visual Anomaly Detection Via Partition Memory Bank Module and Error Estimation	Sep 26, 2022	Anomaly DetectionAnomaly Localization	—Unverified
Composite Convolution: a Flexible Operator for Deep Learning on 3D Point Clouds	Sep 23, 2022	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics	Sep 23, 2022	Anomaly DetectionTime Series	—Unverified
Oracle Analysis of Representations for Deep Open Set Detection	Sep 22, 2022	Anomaly DetectionAutonomous Driving	—Unverified
Query-based Industrial Analytics over Knowledge Graphs with Ontology Reshaping	Sep 22, 2022	Anomaly DetectionData Integration	—Unverified
Self-supervised Learning for Clustering of Wireless Spectrum Activity	Sep 22, 2022	Anomaly DetectionClustering	CodeCode Available
Anomaly Detection on Financial Time Series by Principal Component Analysis and Neural Networks	Sep 22, 2022	Anomaly DetectionImputation	CodeCode Available
Hybrid AI-based Anomaly Detection Model using Phasor Measurement Unit Data	Sep 21, 2022	Anomaly Detection	—Unverified
Contrastive Learning for Time Series on Dynamic Graphs	Sep 21, 2022	Activity RecognitionAnomaly 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