# On Adversarial Mixup Resynthesis

arXiv: https://arxiv.org/abs/1903.02709 · HTML: https://mlsanity.com/paper/1903.02709/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2019.

## Built on by (most cited first, 2 of 2)

- [ReMix: Towards Image-to-Image Translation with Limited Data](https://mlsanity.com/paper/2103.16835/) (2021)
- [ScoreMix: A Scalable Augmentation Strategy for Training GANs with Limited Data](https://mlsanity.com/paper/2210.15137/) (2022)

## Abstract (arXiv)

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 explore the use of such an architecture in the context of semi-supervised learning, where we learn a mixing function whose objective is to produce interpolations of hidden states, or masked combinations of latent representations that are consistent with a conditioned class label. We show quantitative and qualitative evidence that such a formulation is an interesting avenue of research.