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 3041–3050 of 4856 papers

Title	Date	Tasks	Status
BiPOCO: Bi-Directional Trajectory Prediction with Pose Constraints for Pedestrian Anomaly Detection	Jul 5, 2022	Anomaly DetectionAutonomous Driving	CodeCode Available
Transformer based Models for Unsupervised Anomaly Segmentation in Brain MR Images	Jul 5, 2022	Anomaly DetectionAnomaly Segmentation	CodeCode Available
Anomaly Detection with Adversarially Learned Perturbations of Latent Space	Jul 3, 2022	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Augment to Detect Anomalies with Continuous Labelling	Jul 3, 2022	Anomaly DetectionImage Augmentation	—Unverified
A geometric framework for outlier detection in high-dimensional data	Jul 1, 2022	Anomaly DetectionOutlier Detection	—Unverified
DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection	Jun 30, 2022	Anomaly DetectionGraph Anomaly Detection	—Unverified
Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models	Jun 30, 2022	Anomaly Detection	CodeCode Available
A Causal Approach to Detecting Multivariate Time-series Anomalies and Root Causes	Jun 30, 2022	Anomaly DetectionTime Series	—Unverified
Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysis	Jun 30, 2022	Anomaly Detection	—Unverified
Learning Citywide Patterns of Life from Trajectory Monitoring	Jun 30, 2022	Anomaly DetectionAutonomous Vehicles	—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