On Adversarial Mixup Resynthesis
NeurIPS, pp. 4348-4359, 2019.
supervised learningsemi-supervised learning
In this paper, we explore new approaches to combining information encoded within the learned representations of auto-encoders. We explore models that are capable of combining the attributes of multiple inputs such that a resynthesised output is trained to fool an adversarial discriminator for real versus synthesised data. Furthermore, we ...More