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 2591–2600 of 4856 papers

Title	Date	Tasks	Status
Autoencoders for Semivisible Jet Detection	Dec 6, 2021	Anomaly Detection	—Unverified
Autoencoders for unsupervised anomaly detection in high energy physics	Apr 19, 2021	Anomaly DetectionJet Tagging	—Unverified
Autoencoding Binary Classifiers for Supervised Anomaly Detection	Mar 26, 2019	Anomaly DetectionSupervised Anomaly Detection	—Unverified
Autoencoding Features for Aviation Machine Learning Problems	Nov 3, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types	Jun 24, 2020	Anomaly DetectionKnowledge Graphs	—Unverified
Automated Anomaly Detection on European XFEL Klystrons	May 20, 2024	Anomaly DetectionDimensionality Reduction	—Unverified
Automated Antenna Testing Using Encoder-Decoder-based Anomaly Detection	Nov 27, 2021	Anomaly DetectionDecoder	—Unverified
Automated Model Selection for Time-Series Anomaly Detection	Aug 25, 2020	Anomaly Detectionmodel	—Unverified
Automated Processing of eXplainable Artificial Intelligence Outputs in Deep Learning Models for Fault Diagnostics of Large Infrastructures	Mar 19, 2025	Anomaly DetectionExplainable artificial intelligence	—Unverified
Automated Real-time Anomaly Detection in Human Trajectories using Sequence to Sequence Networks	Jul 12, 2019	Anomaly DetectionTime Series	—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	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	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	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	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