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

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Showing 1251–1260 of 4856 papers

Title	Date	Tasks	Status
Artificial Intelligence and Machine Learning in 5G Network Security: Opportunities, advantages, and future research trends	Jul 9, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Arrays of (locality-sensitive) Count Estimators (ACE): High-Speed Anomaly Detection via Cache Lookups	Jun 20, 2017	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
AnoDODE: Anomaly Detection with Diffusion ODE	Oct 10, 2023	Anomaly DetectionAnomaly Localization	—Unverified
Acoustic anomaly detection via latent regularized gaussian mixture generative adversarial networks	Feb 4, 2020	Anomaly DetectionGenerative Adversarial Network	—Unverified
A Robust Likelihood Model for Novelty Detection	Jun 6, 2023	Adversarial AttackAnomaly Detection	—Unverified
Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data	Feb 11, 2025	Anomaly DetectionData Integration	—Unverified
A Robust Autoencoder Ensemble-Based Approach for Anomaly Detection in Text	May 16, 2024	Anomaly DetectionBenchmarking	—Unverified
A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics	Sep 23, 2022	Anomaly DetectionTime Series	—Unverified
An LSTM-Based Predictive Monitoring Method for Data with Time-varying Variability	Sep 5, 2023	Anomaly DetectionPrediction Intervals	—Unverified
Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning	Jun 5, 2020	Anomaly Detectionimage-classification	—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