1 citations · 1 across the 4 of their papers we have counts for
4 papers
SuSana Distancia is all you need: Enforcing class separability in metric learning via two novel distance-based loss functions for few-shot image classification
Mauricio Mendez-Ruiz, Jorge Gonzalez-Zapata, Ivan Reyes-Amezcua +4
Few-shot learning is a challenging area of research that aims to learn new concepts with only a few labeled samples of data. Recent works based on metric-learning approaches levera…
Guided Deep Metric Learning
Jorge Gonzalez-Zapata, Ivan Reyes-Amezcua, Daniel Flores-Araiza +3
Deep Metric Learning (DML) methods have been proven relevant for visual similarity learning. However, they sometimes lack generalization properties because they are trained often u…
On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification
Mauricio Mendez-Ruiz, Francisco Lopez-Tiro, Jonathan El-Beze +5
Deep learning has shown great promise in diverse areas of computer vision, such as image classification, object detection and semantic segmentation, among many others. However, as…
Finding Significant Features for Few-Shot Learning using Dimensionality Reduction
Mauricio Mendez-Ruiz, Ivan Garcia Jorge Gonzalez-Zapata, Gilberto Ochoa-Ruiz +1
Few-shot learning is a relatively new technique that specializes in problems where we have little amounts of data. The goal of these methods is to classify categories that have not…