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 2091–2100 of 4856 papers

Title	Date	Tasks	Status
Coresets for Data Discretization and Sine Wave Fitting	Mar 6, 2022	Anomaly Detection	—Unverified
Copula Quadrant Similarity for Anomaly Scores	Jan 7, 2021	Anomaly Detection	—Unverified
A multi-model approach using XAI and anomaly detection to predict asteroid hazards	Mar 20, 2025	Anomaly Detection	—Unverified
A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories	Jul 28, 2020	Anomaly DetectionAutonomous Driving	—Unverified
Copula-based anomaly scoring and localization for large-scale, high-dimensional continuous data	Dec 4, 2019	Anomaly DetectionMissing Values	—Unverified
cOOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations	Jul 14, 2023	Anomaly DetectionBinary Classification	—Unverified
A Multi-modal one-class generative adversarial network for anomaly detection in manufacturing	May 1, 2019	Anomaly DetectionGenerative Adversarial Network	—Unverified
Convolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detection	Jul 29, 2021	Anomaly DetectionVideo Anomaly Detection	—Unverified
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection	Nov 25, 2021	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Convolutional Recurrent Reconstructive Network for Spatiotemporal Anomaly Detection in Solder Paste Inspection	Aug 22, 2019	Anomaly DetectionDecoder	—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