On Adversarial Mixup Resynthesis
Christopher Beckham, Sina Honari, Vikas Verma, Alex M Lamb, Farnoosh Ghadiri, R Devon Hjelm, Yoshua Bengio, Chris Pal
Abstract
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.
BibTeX
@inproceedings{NEURIPS2019_f708f064,
author = {Beckham, Christopher and Honari, Sina and Verma, Vikas and Lamb, Alex M and Ghadiri, Farnoosh and Hjelm, R Devon and Bengio, Yoshua and Pal, Chris},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {On Adversarial Mixup Resynthesis},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/f708f064faaf32a43e4d3c784e6af9ea-Paper.pdf},
volume = {32},
year = {2019}
}