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 3781–3790 of 4856 papers

Title	Date	Tasks	Status
Multicalibrated Partitions for Importance Weights	Mar 10, 2021	Anomaly DetectionDomain Adaptation	—Unverified
Multi-Class Anomaly Detection based on Regularized Discriminative Coupled hypersphere-based Feature Adaptation	Nov 24, 2023	Anomaly DetectionMulti-class Anomaly Detection	—Unverified
Stack of discriminative autoencoders for multiclass anomaly detection in endoscopy images	Mar 15, 2021	Anomaly DetectionClustering	—Unverified
Anomaly Detection for Scenario-based Insider Activities using CGAN Augmented Data	Feb 15, 2021	Anomaly DetectionGeneral Classification	—Unverified
Multi-Class Multiple Instance Learning for Predicting Precursors to Aviation Safety Events	Mar 10, 2021	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Multi-Contextual Predictions with Vision Transformer for Video Anomaly Detection	Jun 17, 2022	Anomaly DetectionVideo Anomaly Detection	—Unverified
Multi-criteria Anomaly Detection using Pareto Depth Analysis	Dec 1, 2012	Anomaly Detection	—Unverified
Multi-criteria Similarity-based Anomaly Detection using Pareto Depth Analysis	Aug 20, 2015	Anomaly Detection	—Unverified
Multi-feature Reconstruction Network using Crossed-mask Restoration for Unsupervised Industrial Anomaly Detection	Apr 20, 2024	Anomaly DetectionImage Reconstruction	—Unverified
Multilevel Anomaly Detection for Mixed Data	Oct 20, 2016	Anomaly DetectionUnsupervised 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