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 3151–3160 of 4856 papers

Title	Date	Tasks	Status	Hype
Generative Optimization Networks for Memory Efficient Data Generation	Oct 6, 2021	Anomaly DetectionTime Series	CodeCode Available	0
An Efficient Anomaly Detection Approach using Cube Sampling with Streaming Data	Oct 5, 2021	Anomaly DetectionEvent Detection	—Unverified	0
A Multi-Scale A Contrario method for Unsupervised Image Anomaly Detection	Oct 5, 2021	Anomaly Detection	—Unverified	0
Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images	Oct 5, 2021	Anomaly DetectionImage Reconstruction	—Unverified	0
Stochastic tensor space feature theory with applications to robust machine learning	Oct 4, 2021	Anomaly DetectionBIG-bench Machine Learning	—Unverified	0
Probabilistic Robust Autoencoders for Outlier Detection	Oct 1, 2021	Anomaly DetectionOutlier Detection	—Unverified	0
Real-Time Predictive Maintenance using Autoencoder Reconstruction and Anomaly Detection	Oct 1, 2021	Anomaly Detection	—Unverified	0
Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization	Sep 30, 2021	Anomaly ClassificationAnomaly Detection	CodeCode Available	1
A multi-domain splitting framework for time-varying graph structure	Sep 29, 2021	Anomaly Detection	—Unverified	0
ANOMALY DETECTION WITH FRAME-GROUP ATTENTION IN SURVEILLANCE VIDEOS	Sep 29, 2021	Anomaly 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	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