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 1821–1830 of 4856 papers

Title	Date	Tasks	Status
Efficient Client Selection in Federated Learning	Jan 25, 2025	Anomaly DetectionFederated Learning	—Unverified
Efficient Consensus Model based on Proximal Gradient Method applied to Convolutional Sparse Problems	Nov 19, 2020	Anomaly DetectionDictionary Learning	—Unverified
Anomaly Detection with the Voronoi Diagram Evolutionary Algorithm	Oct 27, 2016	Anomaly DetectionGeneral Classification	—Unverified
A2Log: Attentive Augmented Log Anomaly Detection	Sep 20, 2021	Anomaly DetectionData Augmentation	—Unverified
Anomaly Anything: Promptable Unseen Visual Anomaly Generation	Jun 3, 2024	Anomaly DetectionImage Generation	—Unverified
Efficient Dynamic Clustering: Capturing Patterns from Historical Cluster Evolution	Mar 2, 2022	Anomaly DetectionClustering	—Unverified
Efficiently Discovering Frequent Motifs in Large-scale Sensor Data	Jan 2, 2015	Anomaly DetectionClustering	—Unverified
Attention-Based Self-Supervised Feature Learning for Security Data	Mar 24, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Efficient Non-Compression Auto-Encoder for Driving Noise-based Road Surface Anomaly Detection	Nov 22, 2021	Anomaly DetectionFriction	—Unverified
Anomaly Detection with Test Time Augmentation and Consistency Evaluation	Jun 6, 2022	Anomaly DetectionRepresentation 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	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