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 4101–4110 of 4856 papers

Title	Date	Tasks	Status
Anomaly Detection in Particulate Matter Sensor using Hypothesis Pruning Generative Adversarial Network	Dec 2, 2019	Anomaly DetectionGenerative Adversarial Network	—Unverified
An Attribute Oriented Induction based Methodology for Data Driven Predictive Maintenance	Dec 2, 2019	Anomaly DetectionAttribute	—Unverified
Semi-Supervised Learning of Bearing Anomaly Detection via Deep Variational Autoencoders	Dec 2, 2019	Anomaly DetectionFault Diagnosis	—Unverified
XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning	Dec 1, 2019	Anomaly DetectionOutlier Detection	CodeCode Available
ACE -- An Anomaly Contribution Explainer for Cyber-Security Applications	Dec 1, 2019	Anomaly Detection	—Unverified
Learning Representations for Time Series Clustering	Dec 1, 2019	Anomaly DetectionClustering	CodeCode Available
Transfer Anomaly Detection by Inferring Latent Domain Representations	Dec 1, 2019	Anomaly Detection	—Unverified
A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients	Nov 28, 2019	Anomaly Detection	—Unverified
Sparse-GAN: Sparsity-constrained Generative Adversarial Network for Anomaly Detection in Retinal OCT Image	Nov 28, 2019	Anomaly DetectionDeep Learning	—Unverified
Free-riders in Federated Learning: Attacks and Defenses	Nov 28, 2019	Anomaly DetectionFederated Learning	—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