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 3501–3510 of 4856 papers

Title	Date	Tasks	Status
PICASO: Permutation-Invariant Cascaded Attentional Set Operator	Jul 17, 2021	Anomaly DetectionClustering	CodeCode Available
Time Series Anomaly Detection for Smart Grids: A Survey	Jul 16, 2021	Anomaly DetectionSurvey	—Unverified
Contrastive Predictive Coding for Anomaly Detection	Jul 16, 2021	Anomaly DetectionAnomaly Segmentation	—Unverified
Detection of Abnormal Behavior with Self-Supervised Gaze Estimation	Jul 14, 2021	Anomaly DetectionGaze Estimation	—Unverified
Deep learning approaches to Earth Observation change detection	Jul 13, 2021	Anomaly DetectionChange Detection	—Unverified
LATTE: LSTM Self-Attention based Anomaly Detection in Embedded Automotive Platforms	Jul 12, 2021	Anomaly Detection	—Unverified
Attack Rules: An Adversarial Approach to Generate Attacks for Industrial Control Systems using Machine Learning	Jul 11, 2021	Anomaly Detection	—Unverified
Anomaly Detection in Residential Video Surveillance on Edge Devices in IoT Framework	Jul 10, 2021	Anomaly DetectionCPU	—Unverified
Explainable AI (XAI) for PHM of Industrial Asset: A State-of-The-Art, PRISMA-Compliant Systematic Review	Jul 8, 2021	Anomaly DetectionArticles	—Unverified
Anomaly Detection Based on Multiple-Hypothesis Autoencoder	Jul 7, 2021	Anomaly Detection	—Unverified

Show:10 25 50

← PrevPage 351 of 486Next →

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