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 3711–3720 of 4856 papers

Title	Date	Tasks	Status	Hype
Automating Abnormality Detection in Musculoskeletal Radiographs through Deep Learning	Oct 21, 2020	Anomaly DetectionDeep Learning	—Unverified	0
Anomaly Detection in a Large-scale Cloud Platform	Oct 21, 2020	Anomaly DetectionCloud Computing	—Unverified	0
An Empirical Investigation of Contextualized Number Prediction	Oct 20, 2020	Anomaly DetectionPrediction	CodeCode Available	0
Graph Fairing Convolutional Networks for Anomaly Detection	Oct 20, 2020	Anomaly DetectionSemi-supervised Anomaly Detection	CodeCode Available	0
A Federated Learning Approach to Anomaly Detection in Smart Buildings	Oct 20, 2020	Anomaly DetectionFederated Learning	—Unverified	0
Action Sequence Augmentation for Early Graph-based Anomaly Detection	Oct 20, 2020	Anomaly DetectionData Augmentation	CodeCode Available	0
Anomaly Detection on X-Rays Using Self-Supervised Aggregation Learning	Oct 19, 2020	Anomaly Detection	—Unverified	0
Addressing Variance Shrinkage in Variational Autoencoders using Quantile Regression	Oct 18, 2020	Anomaly DetectionLesion Detection	—Unverified	0
Reconstruction by Inpainting for Visual Anomaly Detection	Oct 17, 2020	Anomaly Detection	CodeCode Available	1
On the Usage of Generative Models for Network Anomaly Detection in Multivariate Time-Series	Oct 16, 2020	Anomaly DetectionIntrusion 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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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