most citedSuSana Distancia is all you need: Enforcing class separability in metric learning via two novel distance-based loss functions for few-shot image classification

1 citations · 1 across the 2 of their papers we have counts for

collaborators

2 papers

cs.CV2023★ 1 cited

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…

cs.CV2022

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…