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 2721–2730 of 4856 papers

Title	Date	Tasks	Status
Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety	Feb 8, 2023	Action DetectionAnomaly Detection	—Unverified
Towards Meaningful Anomaly Detection: The Effect of Counterfactual Explanations on the Investigation of Anomalies in Multivariate Time Series	Feb 7, 2023	Anomaly Detectioncounterfactual	—Unverified
Unsupervised Deep Learning for IoT Time Series	Feb 7, 2023	Anomaly DetectionClustering	—Unverified
Label Assisted Autoencoder for Anomaly Detection in Power Generation Plants	Feb 6, 2023	Anomaly ClassificationAnomaly Detection	—Unverified
Integrating Eye-Gaze Data into CXR DL Approaches: A Preliminary study	Feb 6, 2023	Anomaly Detection	—Unverified
An Asymmetric Loss with Anomaly Detection LSTM Framework for Power Consumption Prediction	Feb 5, 2023	Anomaly DetectionLoad Forecasting	—Unverified
Conformalized Semi-supervised Random Forest for Classification and Abnormality Detection	Feb 4, 2023	Anomaly DetectionMedical Diagnosis	CodeCode Available
Unsupervised Ensemble Methods for Anomaly Detection in PLC-based Process Control	Feb 4, 2023	Anomaly Detection	—Unverified
Multivariate Time Series Anomaly Detection via Dynamic Graph Forecasting	Feb 4, 2023	Anomaly DetectionTime Series	—Unverified
Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial Networks	Feb 3, 2023	Anomaly DetectionSemi-supervised Anomaly Detection	CodeCode Available

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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