  Deep learning architectures have achieved promising results in different
areas (e.g., medicine, agriculture, and security). However, using those
powerful techniques in many real applications becomes challenging due to the
large labeled collections required during training. Several works have pursued
solutions to overcome it by proposing strategies that can learn more for less,
e.g., weakly and semi-supervised learning approaches. As these approaches do
not usually address memorization and sensitivity to adversarial examples, this
paper presents three deep metric learning approaches combined with Mixup for
incomplete-supervision scenarios. We show that some state-of-the-art approaches
in metric learning might not work well in such scenarios. Moreover, the
proposed approaches outperform most of them in different datasets.
