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 4331–4340 of 4856 papers

Title	Date	Tasks	Status	Hype
Supervised Anomaly Detection based on Deep Autoregressive Density Estimators	Apr 12, 2019	Anomaly DetectionDensity Estimation	—Unverified	0
Software Based Higher Order Structural Foot Abnormality Detection Using Image Processing	Apr 11, 2019	Anomaly DetectionGeneral Classification	—Unverified	0
Evaluation of a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery	Apr 10, 2019	Anomaly DetectionGeneral Classification	—Unverified	0
Deep Learning for System Trace Restoration	Apr 10, 2019	Anomaly DetectionDeep Learning	—Unverified	0
Functional Isolation Forest	Apr 9, 2019	Anomaly Detection	CodeCode Available	1
Place-specific Background Modeling Using Recursive Autoencoders	Apr 7, 2019	Anomaly DetectionChange Detection	—Unverified	0
The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation	Apr 5, 2019	Anomaly DetectionAutonomous Driving	CodeCode Available	0
Learning Representations from Healthcare Time Series Data for Unsupervised Anomaly Detection	Apr 4, 2019	Anomaly DetectionClustering	—Unverified	0
Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection	Apr 4, 2019	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	0
Efficient GAN-based method for cyber-intrusion detection	Apr 4, 2019	Anomaly DetectionGenerative Adversarial Network	—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