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 681–690 of 4856 papers

Title	Date	Tasks	Status	Hype
A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges	Oct 26, 2021	Anomaly DetectionNovelty Detection	CodeCode Available	1
Practical Galaxy Morphology Tools from Deep Supervised Representation Learning	Oct 25, 2021	Anomaly DetectionAstronomy	CodeCode Available	1
Generalized Out-of-Distribution Detection: A Survey	Oct 21, 2021	Anomaly DetectionAutonomous Driving	CodeCode Available	1
Learning Not to Reconstruct Anomalies	Oct 19, 2021	Anomaly DetectionOne-Class Classification	CodeCode Available	1
Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection	Oct 19, 2021	Anomaly DetectionOne-Class Classification	CodeCode Available	1
Anomaly Detection in Multi-Agent Trajectories for Automated Driving	Oct 15, 2021	Anomaly Detection	CodeCode Available	1
Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection	Oct 6, 2021	Anomaly DetectionDefect Detection	CodeCode Available	1
Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization	Sep 30, 2021	Anomaly ClassificationAnomaly Detection	CodeCode Available	1
An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series	Sep 23, 2021	Anomaly DetectionTime Series	CodeCode Available	1
DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications	Sep 23, 2021	Anomaly Detection	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	GLASS	Detection AUROC	99.9	—	Unverified
5	UniNet	Detection AUROC	99.9	—	Unverified
6	HETMM	Detection AUROC	99.8	—	Unverified
7	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	PBAS	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	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	DDAD	Detection AUROC	98.9	—	Unverified
6	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
7	DiffusionAD	Detection AUROC	98.8	—	Unverified
8	GLASS	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