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.LG2022

MACFE: A Meta-learning and Causality Based Feature Engineering Framework

Ivan Reyes-Amezcua, Daniel Flores-Araiza, Gilberto Ochoa-Ruiz +2

Feature engineering has become one of the most important steps to improve model prediction performance, and to produce quality datasets. However, this process requires non-trivial…