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 3551–3560 of 4856 papers

Title	Date	Tasks	Status
Towards Deep Industrial Transfer Learning for Anomaly Detection on Time Series Data	Jun 9, 2021	Anomaly DetectionDeep Learning	—Unverified
Dual-Modality Vehicle Anomaly Detection via Bilateral Trajectory Tracing	Jun 9, 2021	Anomaly Detectionvehicle detection	CodeCode Available
Spot the Difference: Detection of Topological Changes via Geometric Alignment	Jun 9, 2021	Anomaly DetectionDomain Adaptation	CodeCode Available
Deep Random Projection Outlyingness for Unsupervised Anomaly Detection	Jun 8, 2021	Anomaly DetectionCompressive Sensing	—Unverified
Shifting Transformation Learning for Out-of-Distribution Detection	Jun 7, 2021	Anomaly DetectionContrastive Learning	—Unverified
FlexParser -- the adaptive log file parser for continuous results in a changing world	Jun 6, 2021	Anomaly DetectionIntrusion Detection	—Unverified
Heart Sound Classification Considering Additive Noise and Convolutional Distortion	Jun 3, 2021	Anomaly DetectionClassification	—Unverified
Effort-free Automated Skeletal Abnormality Detection of Rat Fetuses on Whole-body Micro-CT Scans	Jun 3, 2021	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Data augmentation and pre-trained networks for extremely low data regimes unsupervised visual inspection	Jun 2, 2021	Anomaly DetectionData Augmentation	—Unverified
Heterogeneous Noisy Short Signal Camouflage in Multi-Domain Environment Decision-Making	Jun 2, 2021	Anomaly DetectionDecision Making	—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