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 3691–3700 of 4856 papers

Title	Date	Tasks	Status
A review of neural network algorithms and their applications in supercritical extraction	Oct 31, 2020	Anomaly DetectionChemical Process	—Unverified
Higher-Order Moment-Based Anomaly Detection	Oct 30, 2020	Anomaly Detection	—Unverified
Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection	Oct 29, 2020	Anomaly DetectionClustering	—Unverified
Self-awareness in intelligent vehicles: Feature based dynamic Bayesian models for abnormality detection	Oct 29, 2020	Anomaly DetectionAutonomous Vehicles	—Unverified
LSTM for Model-Based Anomaly Detection in Cyber-Physical Systems	Oct 29, 2020	Anomaly Detection	—Unverified
A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata	Oct 29, 2020	Anomaly Detection	—Unverified
Interpretable Machine Learning Models for Predicting and Explaining Vehicle Fuel Consumption Anomalies	Oct 28, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Self-awareness in Intelligent Vehicles: Experience Based Abnormality Detection	Oct 28, 2020	Anomaly DetectionSemantic Segmentation	—Unverified
Dynamic Bayesian Approach for decision-making in Ego-Things	Oct 28, 2020	Anomaly DetectionClustering	—Unverified
Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder	Oct 28, 2020	Anomaly DetectionDimensionality Reduction	—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