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 4061–4070 of 4856 papers

Title	Date	Tasks	Status
OIAD: One-for-all Image Anomaly Detection with Disentanglement Learning	Jan 18, 2020	AllAnomaly Detection	—Unverified
Neighborhood Structure Assisted Non-negative Matrix Factorization and its Application in Unsupervised Point-wise Anomaly Detection	Jan 17, 2020	Anomaly DetectionDimensionality Reduction	—Unverified
Simulation Assisted Likelihood-free Anomaly Detection	Jan 14, 2020	Anomaly DetectionMORPH	CodeCode Available
Unsupervised Learning of the Set of Local Maxima	Jan 14, 2020	Anomaly DetectionGeneral Classification	—Unverified
Anomaly Detection with Density Estimation	Jan 14, 2020	Anomaly DetectionDensity Estimation	CodeCode Available
Unsupervised Distribution Learning for Lunar Surface Anomaly Detection	Jan 14, 2020	Anomaly DetectionDensity Estimation	—Unverified
Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift	Jan 13, 2020	Active LearningAnomaly Detection	—Unverified
Deep learning achieves perfect anomaly detection on 108,308 retinal images including unlearned diseases	Jan 13, 2020	Anomaly Detection	CodeCode Available
Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing	Jan 10, 2020	Anomaly DetectionEdge-computing	—Unverified
Granular Learning with Deep Generative Models using Highly Contaminated Data	Jan 6, 2020	Anomaly DetectionGeneral Classification	—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