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 2111–2120 of 4856 papers

Title	Date	Tasks	Status
Control theoretically explainable application of autoencoder methods to fault detection in nonlinear dynamic systems	Aug 2, 2022	Anomaly DetectionFault Detection	—Unverified
Controllable RANSAC-based Anomaly Detection via Hypothesis Testing	Oct 19, 2024	Anomaly Detectionregression	—Unverified
Anomaly Detection via Learning-Based Sequential Controlled Sensing	Nov 30, 2023	Anomaly DetectionDecision Making	—Unverified
A Deep Learning Approach to Video Anomaly Detection using Convolutional Autoencoders	Nov 7, 2023	Anomaly DetectionDecoder	—Unverified
Contrastive Structured Anomaly Detection for Gaussian Graphical Models	May 2, 2016	Anomaly Detection	—Unverified
Anomaly Detection via Gumbel Noise Score Matching	Apr 6, 2023	Anomaly DetectionSegmentation	—Unverified
Contrastive-Regularized U-Net for Video Anomaly Detection	Apr 11, 2023	Anomaly DetectionAnomaly Detection In Surveillance Videos	—Unverified
A Multi-Level Approach for Class Imbalance Problem in Federated Learning for Remote Industry 4.0 Applications	Sep 24, 2024	Anomaly DetectionFederated Learning	—Unverified
A Combination of Temporal Sequence Learning and Data Description for Anomaly-based NIDS	Jun 7, 2019	Anomaly DetectionIntrusion Detection	—Unverified
G-CMP: Graph-enhanced Contextual Matrix Profile for unsupervised anomaly detection in sensor-based remote health monitoring	Nov 29, 2022	Anomaly Detectiongraph construction	—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	HETMM	Detection AUROC	99.8	—	Unverified
7	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	PBAS	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	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	DDAD	Detection AUROC	98.9	—	Unverified
6	Dinomaly ViT-L (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