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 1411–1420 of 4856 papers

Title	Date	Tasks	Status
EVBattery: A Large-Scale Electric Vehicle Dataset for Battery Health and Capacity Estimation	Jan 28, 2022	Anomaly DetectionCapacity Estimation	—Unverified
Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving	Jul 5, 2021	Anomaly DetectionTime Series	—Unverified
Detecting Backdoors in Neural Networks Using Novel Feature-Based Anomaly Detection	Nov 4, 2020	Anomaly DetectionData Augmentation	—Unverified
An Autonomous Drone Swarm for Detecting and Tracking Anomalies among Dense Vegetation	Jul 15, 2024	Anomaly Detection	—Unverified
Detecting Clusters of Anomalies on Low-Dimensional Feature Subsets with Application to Network Traffic Flow Data	Jun 10, 2015	Anomaly DetectionGroup Anomaly Detection	—Unverified
A Novel Representation of Periodic Pattern and Its Application to Untrained Anomaly Detection	Sep 9, 2024	Anomaly Detection	—Unverified
An Automated Machine Learning Approach for Detecting Anomalous Peak Patterns in Time Series Data from a Research Watershed in the Northeastern United States Critical Zone	Sep 14, 2023	Anomaly DetectionHyperparameter Optimization	—Unverified
A Discriminative Framework for Anomaly Detection in Large Videos	Sep 28, 2016	Anomaly DetectionDensity Estimation	—Unverified
Detecting Compromised IoT Devices Using Autoencoders with Sequential Hypothesis Testing	Apr 21, 2024	Anomaly Detection	—Unverified
A Novel Data Pre-processing Technique: Making Data Mining Robust to Different Units and Scales of Measurement	Nov 8, 2021	Anomaly Detection	—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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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