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 4491–4500 of 4856 papers

Title	Date	Tasks	Status
Neuromorphic Architecture for the Hierarchical Temporal Memory	Aug 17, 2018	Anomaly DetectionSelf-Learning	—Unverified
Metric Learning for Novelty and Anomaly Detection	Aug 16, 2018	Anomaly DetectionMetric Learning	CodeCode Available
Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images	Aug 10, 2018	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
Image Anomalies: a Review and Synthesis of Detection Methods	Aug 7, 2018	Anomaly Detection	—Unverified
Robust Spectral Filtering and Anomaly Detection	Aug 3, 2018	Anomaly Detection	—Unverified
Anomaly Detection via Minimum Likelihood Generative Adversarial Networks	Aug 1, 2018	Anomaly DetectionIntrusion Detection	—Unverified
Scalable Multi-Task Gaussian Process Tensor Regression for Normative Modeling of Structured Variation in Neuroimaging Data	Jul 31, 2018	Anomaly DetectionMulti-Task Learning	—Unverified
Call Detail Records Driven Anomaly Detection and Traffic Prediction in Mobile Cellular Networks	Jul 30, 2018	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Detector monitoring with artificial neural networks at the CMS experiment at the CERN Large Hadron Collider	Jul 27, 2018	Anomaly Detection	CodeCode Available
Anomaly detection in static networks using egonets	Jul 24, 2018	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