68 citations · 68 across the 2 of their papers we have counts for
2 papers
cs.LG2022
No Pairs Left Behind: Improving Metric Learning with Regularized Triplet Objective
A. Ali Heydari, Naghmeh Rezaei, Daniel J. McDuff +1
We propose a novel formulation of the triplet objective function that improves metric learning without additional sample mining or overhead costs. Our approach aims to explicitly r…
cs.LG2019★ 68 cited
SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions
A. Ali Heydari, Craig A. Thompson, Asif Mehmood
Adaptive loss function formulation is an active area of research and has gained a great deal of popularity in recent years, following the success of deep learning. However, existin…