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 3141–3150 of 4856 papers

Title	Date	Tasks	Status
Sparse Binary Transformers for Multivariate Time Series Modeling	Aug 9, 2023	Anomaly DetectionTime Series	—Unverified
Sparse Coding-inspired GAN for Weakly Supervised Hyperspectral Anomaly Detection	Jan 1, 2021	Anomaly DetectionDecoder	—Unverified
Sparse-GAN: Sparsity-constrained Generative Adversarial Network for Anomaly Detection in Retinal OCT Image	Nov 28, 2019	Anomaly DetectionDeep Learning	—Unverified
Sparse Modelling for Feature Learning in High Dimensional Data	Sep 28, 2024	Anomaly DetectionDefect Detection	—Unverified
Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking	Mar 31, 2016	Anomaly DetectionDimensionality Reduction	—Unverified
Sparsity in Multivariate Extremes with Applications to Anomaly Detection	Jul 21, 2015	Anomaly DetectionDimensionality Reduction	—Unverified
Spatially Regularized Graph Attention Autoencoder Framework for Detecting Rainfall Extremes	Nov 12, 2024	Anomaly DetectionGraph Attention	—Unverified
Spatially-resolved hyperlocal weather prediction and anomaly detection using IoT sensor networks and machine learning techniques	Oct 17, 2023	Anomaly DetectionDecision Making	—Unverified
Spatially-weighted Anomaly Detection	Oct 5, 2018	Anomaly Detection	—Unverified
Spatially-weighted Anomaly Detection with Regression Model	Mar 23, 2019	Anomaly Detectionmodel	—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