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 2181–2190 of 4856 papers

Title	Date	Tasks	Status	Hype
A Bi-LSTM Autoencoder Framework for Anomaly Detection -- A Case Study of a Wind Power Dataset	Mar 17, 2023	Anomaly DetectionTime Series	—Unverified	0
Wireless Sensor Networks anomaly detection using Machine Learning: A Survey	Mar 15, 2023	Anomaly Detection	—Unverified	0
Towards Phytoplankton Parasite Detection Using Autoencoders	Mar 15, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	0
DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection	Mar 15, 2023	Anomaly DetectionDenoising	CodeCode Available	2
Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection	Mar 15, 2023	Anomaly DetectionAnomaly Segmentation	—Unverified	0
Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insights	Mar 14, 2023	Anomaly DetectionContinual Learning	CodeCode Available	0
Network Anomaly Detection Using Federated Learning	Mar 13, 2023	Anomaly DetectionFederated Learning	—Unverified	0
Spacecraft Anomaly Detection with Attention Temporal Convolution Network	Mar 13, 2023	Anomaly DetectionGraph Attention	CodeCode Available	1
Hallucinated Heartbeats: Anomaly-Aware Remote Pulse Estimation	Mar 11, 2023	Anomaly DetectionFace Presentation Attack Detection	CodeCode Available	0
Interpretable Outlier Summarization	Mar 11, 2023	Anomaly DetectionOutlier Detection	—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	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