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 3481–3490 of 4856 papers

Title	Date	Tasks	Status
Unsupervised Event Outlier Detection in Continuous Time	Nov 25, 2024	Anomaly DetectionData Augmentation	—Unverified
Unsupervised Feature Construction for Anomaly Detection in Time Series -- An Evaluation	Jan 14, 2025	Anomaly DetectionTime Series	—Unverified
Unsupervised Industrial Anomaly Detection via Pattern Generative and Contrastive Networks	Jul 20, 2022	Anomaly DetectionAnomaly Localization	—Unverified
Unsupervised KPIs-Based Clustering of Jobs in HPC Data Centers	Dec 11, 2023	Anomaly DetectionClustering	—Unverified
Unsupervised Learning Approach to Anomaly Detection in Gravitational Wave Data	Nov 29, 2024	Anomaly DetectionTime Series	—Unverified
Unsupervised Learning of Distributional Properties can Supplement Human Labeling and Increase Active Learning Efficiency in Anomaly Detection	Jul 13, 2023	Active LearningAnomaly Detection	—Unverified
Unsupervised Learning of Multi-level Structures for Anomaly Detection	Apr 25, 2021	AllAnomaly Detection	—Unverified
Unsupervised Learning of the Set of Local Maxima	Jan 14, 2020	Anomaly DetectionGeneral Classification	—Unverified
Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra	Jan 7, 2022	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges	Sep 19, 2017	Anomaly DetectionBIG-bench Machine Learning	—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