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 3511–3520 of 4856 papers

Title	Date	Tasks	Status	Hype
Neural Network-based Quantization for Network Automation	Mar 4, 2021	Anomaly DetectionDeep Learning	—Unverified	0
Event-Based Dynamic Banking Network Exploration for Economic Anomaly Detection	Mar 4, 2021	Anomaly Detection	—Unverified	0
Helicopter Track Identification with Autoencoder	Mar 3, 2021	Anomaly DetectionSelf-Supervised Learning	—Unverified	0
Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and Algorithms	Mar 3, 2021	Anomaly DetectionSelf-Driving Cars	CodeCode Available	1
A Body Part Embedding Model With Datasets for Measuring 2D Human Motion Similarity	Mar 2, 2021	Action AnalysisAction Recognition	CodeCode Available	1
AdeNet: Deep learning architecture that identifies damaged electrical insulators in power lines	Mar 2, 2021	Anomaly DetectionTransfer Learning	CodeCode Available	0
Image/Video Deep Anomaly Detection: A Survey	Mar 2, 2021	Anomaly DetectionEvent Detection	—Unverified	0
Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection	Mar 1, 2021	Anomaly DetectionFew-Shot Learning	—Unverified	0
Online anomaly detection using statistical leverage for streaming business process events	Mar 1, 2021	Anomaly Detection	CodeCode Available	0
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning	Feb 27, 2021	Anomaly DetectionContrastive Learning	CodeCode Available	1

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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