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 2581–2590 of 4856 papers

Title	Date	Tasks	Status
Anomaly Detection Using One-Class SVM for Logs of Juniper Router Devices	May 21, 2023	Anomaly Detection	—Unverified
LightESD: Fully-Automated and Lightweight Anomaly Detection Framework for Edge Computing	May 20, 2023	Anomaly DetectionCPU	—Unverified
MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detection	May 18, 2023	Anomaly DetectionFew-Shot Learning	CodeCode Available
Imbalanced Aircraft Data Anomaly Detection	May 17, 2023	Anomaly DetectionTime Series	—Unverified
Reconstruction Error-based Anomaly Detection with Few Outlying Examples	May 17, 2023	Anomaly DetectionSemi-supervised Anomaly Detection	—Unverified
A Probabilistic Transformation of Distance-Based Outliers	May 16, 2023	Anomaly DetectionOutlier Detection	CodeCode Available
Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptation	May 15, 2023	Anomaly DetectionDrift Detection	—Unverified
Evaluation Strategy of Time-series Anomaly Detection with Decay Function	May 15, 2023	Anomaly DetectionTime Series	—Unverified
A Dataset Fusion Algorithm for Generalised Anomaly Detection in Homogeneous Periodic Time Series Datasets	May 14, 2023	Anomaly DetectionComputational Efficiency	—Unverified
Voxel-wise classification for porosity investigation of additive manufactured parts with 3D unsupervised and (deeply) supervised neural networks	May 13, 2023	Anomaly Detection	CodeCode Available

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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