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 1391–1400 of 4856 papers

Title	Date	Tasks	Status
A Comprehensive Study of Machine Learning Techniques for Log-Based Anomaly Detection	Jul 31, 2023	Anomaly DetectionDeep Learning	—Unverified
APALU: A Trainable, Adaptive Activation Function for Deep Learning Networks	Feb 13, 2024	Anomaly DetectionDeep Learning	—Unverified
An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs	May 4, 2021	Action RecognitionAnomaly Detection	—Unverified
A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems	Jun 8, 2022	Anomaly DetectionArticles	—Unverified
A Bayesian Ensemble for Unsupervised Anomaly Detection	Oct 24, 2016	Anomaly DetectionClustering	—Unverified
Detecting Anomalies Through Contrast in Heterogeneous Data	Apr 2, 2021	Anomaly DetectionContrastive Learning	—Unverified
A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection	Feb 18, 2019	Anomaly DetectionChange Point Detection	—Unverified
ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels	Aug 24, 2022	Anomaly DetectionMixture-of-Experts	—Unverified
A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions	Jul 11, 2023	Anomaly DetectionDeep Learning	—Unverified
Anytime Hierarchical Clustering	Apr 13, 2014	Anomaly DetectionClustering	—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