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 4361–4370 of 4856 papers

Title	Date	Tasks	Status	Hype
adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detection	Mar 3, 2019	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	0
Unsupervised Traffic Accident Detection in First-Person Videos	Mar 2, 2019	Anomaly DetectionAutonomous Driving	CodeCode Available	1
Deep Generative Design: Integration of Topology Optimization and Generative Models	Mar 1, 2019	Anomaly DetectionDeep Learning	—Unverified	0
Unsupervised Abnormality Detection through Mixed Structure Regularization (MSR) in Deep Sparse Autoencoders	Feb 28, 2019	Anomaly DetectionDenoising	—Unverified	0
Anomaly Detection for an E-commerce Pricing System	Feb 25, 2019	Anomaly DetectionModel Selection	—Unverified	0
Towards Corner Case Detection for Autonomous Driving	Feb 25, 2019	Anomaly DetectionAutonomous Driving	—Unverified	0
Bayesian Anomaly Detection and Classification	Feb 22, 2019	Anomaly DetectionClassification	—Unverified	0
Real-time PCG Anomaly Detection by Adaptive 1D Convolutional Neural Networks	Feb 19, 2019	Anomaly DetectionDiagnostic	—Unverified	0
Anomaly Detection with Adversarial Dual Autoencoders	Feb 19, 2019	Anomaly DetectionImage Generation	CodeCode Available	0
A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection	Feb 18, 2019	Anomaly DetectionChange Point 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