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 3471–3480 of 4856 papers

Title	Date	Tasks	Status
Unsupervised deep learning techniques for powdery mildew recognition based on multispectral imaging	Dec 20, 2021	Anomaly DetectionDeep Learning	—Unverified
Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics	Feb 13, 2023	Anomaly DetectionOne-Class Classification	—Unverified
Unsupervised Detection of Anomalous Sound for Machine Condition Monitoring using Fully Connected U-Net	Jun 29, 2021	Anomaly Detection	—Unverified
Unsupervised Detection of Fetal Brain Anomalies using Denoising Diffusion Models	Aug 7, 2024	Anomaly DetectionDenoising	—Unverified
Unsupervised Detection of Lung Nodules in Chest Radiography Using Generative Adversarial Networks	Aug 4, 2021	Anomaly DetectionDecoder	—Unverified
Unsupervised Dictionary Learning for Anomaly Detection	Feb 29, 2020	Anomaly DetectionDictionary Learning	—Unverified
Unsupervised Distance Metric Learning for Anomaly Detection Over Multivariate Time Series	Mar 4, 2024	Anomaly DetectionDynamic Time Warping	—Unverified
Unsupervised Distribution Learning for Lunar Surface Anomaly Detection	Jan 14, 2020	Anomaly DetectionDensity Estimation	—Unverified
Unsupervised Driver Behavior Profiling leveraging Recurrent Neural Networks	Aug 11, 2021	Anomaly Detection	—Unverified
Unsupervised Ensemble Methods for Anomaly Detection in PLC-based Process Control	Feb 4, 2023	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	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