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 3951–3960 of 4856 papers

Title	Date	Tasks	Status	Hype
Semi-supervised Deep Embedded Clustering with Anomaly Detection for Semantic Frame Induction	May 1, 2020	Anomaly DetectionClustering	CodeCode Available	0
Treating Dialogue Quality Evaluation as an Anomaly Detection Problem	May 1, 2020	Anomaly DetectionDialogue Evaluation	—Unverified	0
Learning Compliance Adaptation in Contact-Rich Manipulation	May 1, 2020	Anomaly DetectionContact-rich Manipulation	—Unverified	0
The 4th AI City Challenge	Apr 30, 2020	Anomaly DetectionComputational Efficiency	—Unverified	0
Data-Driven Construction of Data Center Graph of Things for Anomaly Detection	Apr 27, 2020	Anomaly DetectionGraph Neural Network	—Unverified	0
Real-Time Anomaly Detection in Data Centers for Log-based Predictive Maintenance using an Evolving Fuzzy-Rule-Based Approach	Apr 25, 2020	Anomaly Detection	—Unverified	0
A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis	Apr 25, 2020	Anomaly DetectionDiagnostic	—Unverified	0
Local Adaptation Improves Accuracy of Deep Learning Model for Automated X-Ray Thoracic Disease Detection : A Thai Study	Apr 23, 2020	Anomaly DetectionMedical Diagnosis	—Unverified	0
How to find a unicorn: a novel model-free, unsupervised anomaly detection method for time series	Apr 23, 2020	Anomaly DetectionRespiratory Failure	CodeCode Available	1
Real-time Detection of Clustered Events in Video-imaging data with Applications to Additive Manufacturing	Apr 23, 2020	Anomaly Detectionregression	—Unverified	0

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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