@inproceedings{hjelm2018boundary,
author = {Hjelm, R. Devon and Jacob, Athul Paul and Trischler, Adam and Che, Gerry and Cho, Kyunghyun and Bengio, Yoshua},
title = {Boundary Seeking GANs},
booktitle = {ICLR 2018 Conference},
year = {2018},
month = {February},
abstract = {Generative adversarial networks are a learning framework that rely on training a discriminator to estimate a measure of difference between a target and generated distributions. GANs, as normally formulated, rely on the generated samples being completely differentiable w.r.t. the generative parameters, and thus do not work for discrete data. We introduce a method for training GANs with discrete data that uses the estimated difference measure from the discriminator to compute importance weights for generated samples, thus providing a policy gradient for training the generator. The importance weights have a strong connection to the decision boundary of the discriminator, and we call our method boundary-seeking GANs (BGANs). We demonstrate the effectiveness of the proposed algorithm with discrete image and character-based natural language generation. In addition, the boundary-seeking objective extends to continuous data, which can be used to improve stability of training, and we demonstrate this on Celeba, Large-scale Scene Understanding (LSUN) bedrooms, and Imagenet without conditioning.},
url = {http://approjects.co.za/?big=en-us/research/publication/boundary-seeking-gans/},
edition = {ICLR 2018 Conference},
}