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 3171–3180 of 4856 papers

Title	Date	Tasks	Status	Hype
Y-GAN: Learning Dual Data Representations for Efficient Anomaly Detection	Sep 28, 2021	Anomaly DetectionDisentanglement	—Unverified	0
Anomaly Detection for High-Dimensional Data Using Large Deviations Principle	Sep 28, 2021	Anomaly DetectionTime Series	—Unverified	0
An Automated Data Engineering Pipeline for Anomaly Detection of IoT Sensor Data	Sep 28, 2021	Anomaly DetectionBIG-bench Machine Learning	—Unverified	0
An Energy Efficient Health Monitoring Approach with Wireless Body Area Networks	Sep 27, 2021	Anomaly Detection	—Unverified	0
Visual Anomaly Detection for Images: A Survey	Sep 27, 2021	Anomaly DetectionDeep Learning	—Unverified	0
Distributed Deep Learning for Persistent Monitoring of agricultural Fields	Sep 24, 2021	Anomaly DetectionDeep Learning	—Unverified	0
Quantifying point cloud realism through adversarially learned latent representations	Sep 24, 2021	Anomaly DetectionMetric Learning	—Unverified	0
An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series	Sep 23, 2021	Anomaly DetectionTime Series	CodeCode Available	1
Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks	Sep 23, 2021	Anomaly DetectionAttribute	—Unverified	0
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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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