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 2101–2110 of 4856 papers

Title	Date	Tasks	Status
Convolutional Neural Network Design and Evaluation for Real-Time Multivariate Time Series Fault Detection in Spacecraft Attitude Sensors	Oct 11, 2024	Anomaly DetectionFault Detection	—Unverified
Convolutional Graph Auto-encoder: A Deep Generative Neural Architecture for Probabilistic Spatio-temporal Solar Irradiance Forecasting	Sep 10, 2018	Anomaly DetectionBayesian Inference	—Unverified
A deep learning-based approach for the automated surface inspection of copper clad laminate images	Sep 19, 2020	Anomaly DetectionDefect Detection	—Unverified
A2B-GAN: Utilizing Unannotated Anomalous Images for Anomaly Detection in Medical Image Analysis	Sep 29, 2021	Anomaly DetectionImage-to-Image Translation	—Unverified
GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models	Jun 26, 2025	Anomaly DetectionBrain Tumor Segmentation	—Unverified
GDFlow: Anomaly Detection with NCDE-based Normalizing Flow for Advanced Driver Assistance System	Sep 9, 2024	Anomaly Detection	—Unverified
Convolutional Ensembling based Few-Shot Defect Detection Technique	Aug 5, 2022	Anomaly DetectionDefect Detection	—Unverified
Convolutional Autoencoders for Data Compression and Anomaly Detection in Small Satellite Technologies	Apr 29, 2025	Anomaly DetectionData Compression	—Unverified
Anomaly Detection via Minimum Likelihood Generative Adversarial Networks	Aug 1, 2018	Anomaly DetectionIntrusion Detection	—Unverified
Control theoretically explainable application of autoencoder methods to fault detection in nonlinear dynamic systems	Aug 2, 2022	Anomaly DetectionFault 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	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