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 1021–1030 of 4856 papers

Title	Date	Tasks	Status
Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors	Feb 23, 2022	3D Anomaly Detection3D Anomaly Detection and Segmentation	—Unverified
Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics	Sep 21, 2022	Anomaly DetectionBenchmarking	—Unverified
Anomaly Detection Dataset for Industrial Control Systems	May 11, 2023	Anomaly DetectionIntrusion Detection	—Unverified
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types	Jun 24, 2020	Anomaly DetectionKnowledge Graphs	—Unverified
A Framework for Verifiable and Auditable Federated Anomaly Detection	Mar 15, 2022	Anomaly DetectionEnsemble Learning	—Unverified
Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks	May 27, 2017	Anomaly DetectionGeneral Classification	—Unverified
Autoencoding Features for Aviation Machine Learning Problems	Nov 3, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Anomaly Detection by Robust Statistics	Jul 31, 2017	Anomaly DetectionClustering	—Unverified
Autoencoding Binary Classifiers for Supervised Anomaly Detection	Mar 26, 2019	Anomaly DetectionSupervised Anomaly Detection	—Unverified
Autoencoders for unsupervised anomaly detection in high energy physics	Apr 19, 2021	Anomaly DetectionJet Tagging	—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	GLASS	Detection AUROC	99.9	—	Unverified
5	UniNet	Detection AUROC	99.9	—	Unverified
6	HETMM	Detection AUROC	99.8	—	Unverified
7	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	PBAS	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	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	DDAD	Detection AUROC	98.9	—	Unverified
6	Dinomaly ViT-L (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