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 3291–3300 of 4856 papers

Title	Date	Tasks	Status	Hype
On Generalization of Graph Autoencoders with Adversarial Training	Jul 6, 2021	Anomaly DetectionClustering	CodeCode Available	0
Detecting Outliers with Poisson Image Interpolation	Jul 6, 2021	Anomaly DetectionImage Reconstruction	CodeCode Available	0
Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving	Jul 5, 2021	Anomaly DetectionTime Series	—Unverified	0
A Typology of Data Anomalies	Jul 4, 2021	Anomaly Detection	—Unverified	0
Clustering of Time Series Data with Prior Geographical Information	Jul 3, 2021	Anomaly DetectionClustering	—Unverified	0
Online learning of windmill time series using Long Short-term Cognitive Networks	Jul 1, 2021	Anomaly DetectionScheduling	—Unverified	0
One-class Steel Detector Using Patch GAN Discriminator for Visualising Anomalous Feature Map	Jun 30, 2021	Anomaly DetectionDefect Detection	—Unverified	0
Anomaly Detection: How to Artificially Increase your F1-Score with a Biased Evaluation Protocol	Jun 30, 2021	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	0
Unsupervised Detection of Anomalous Sound for Machine Condition Monitoring using Fully Connected U-Net	Jun 29, 2021	Anomaly Detection	—Unverified	0
Enhancing the Analysis of Software Failures in Cloud Computing Systems with Deep Learning	Jun 29, 2021	Anomaly DetectionCloud Computing	CodeCode Available	1

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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