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 2781–2790 of 4856 papers

Title	Date	Tasks	Status
A Novel Self-Supervised Learning-Based Anomaly Node Detection Method Based on an Autoencoder in Wireless Sensor Networks	Dec 26, 2022	Anomaly DetectionGraph Neural Network	CodeCode Available
Understanding Ethics, Privacy, and Regulations in Smart Video Surveillance for Public Safety	Dec 25, 2022	Action RecognitionAnomaly Detection	—Unverified
EndoBoost: a plug-and-play module for false positive suppression during computer-aided polyp detection in real-world colonoscopy (with dataset)	Dec 23, 2022	Anomaly DetectionDensity Estimation	—Unverified
Mesh of Things (MoT) Network-Driven Anomaly Detection in Connected Objects	Dec 23, 2022	Anomaly Detection	—Unverified
Anomaly Detection using Ensemble Classification and Evidence Theory	Dec 23, 2022	Anomaly DetectionClassification	—Unverified
Supervised Anomaly Detection Method Combining Generative Adversarial Networks and Three-Dimensional Data in Vehicle Inspections	Dec 22, 2022	Anomaly DetectionSupervised Anomaly Detection	—Unverified
Machine Learning with Probabilistic Law Discovery: A Concise Introduction	Dec 22, 2022	Anomaly DetectionTime Series	—Unverified
Joint Embedding of 2D and 3D Networks for Medical Image Anomaly Detection	Dec 21, 2022	Anomaly DetectionSelf-Supervised Learning	—Unverified
LogAnMeta: Log Anomaly Detection Using Meta Learning	Dec 21, 2022	Anomaly DetectionMeta-Learning	—Unverified
Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series	Dec 21, 2022	Anomaly DetectionTime Series	CodeCode Available

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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