GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution

Generative Adversarial Networks (GAN) have limitations when the goal is to generate sequences of discrete elements. The reason for this is that samples from a distribution on discrete objects such as the multinomial are not differentiable with respect to the distribution parameters. This problem can be avoided by using the Gumbel-softmax distribution, which is a continuous approximation to a multinomial distribution parameterized in terms of the softmax function. In this work, we evaluate the performance of GANs based on recurrent neural networks with Gumbel-softmax output distributions in the task of generating sequences of discrete elements

Generating Multi-Categorical Samples with Generative Adversarial Networks

We propose a method to train generative adversarial networks on mutivariate feature vectors representing multiple categorical values. In contrast to the continuous domain, where GAN-based methods have delivered considerable results, GANs struggle to perform equally well on discrete data. We propose and compare several architectures based on multiple (Gumbel) softmax output layers taking into account the structure of the data. We evaluate the performance of our architecture on datasets with different sparsity, number of features, ranges of categorical values, and dependencies among the features. Our proposed architecture and method outperforms existing models

Stylistic scene enhancement GAN: Mixed stylistic enhancement generation for 3D indoor scenes

In this paper, we present stylistic scene enhancement GAN, SSE-GAN, a conditional Wasserstein GAN-based approach to automatic generation of mixed stylistic enhancements for 3D indoor scenes. An enhancement indicates factors that can influence the style of an indoor scene such as furniture colors and occurrence of small objects. To facilitate network training, we propose a novel enhancement feature encoding method, which represents an enhancement by a multi-one-hot vector, and effectively accommodates different enhancement factors. A Gumbel-Softmax module is introduced in the generator network to enable the generation of high fidelity enhancement features that can better confuse the discriminator. Experiments show that our approach is superior to the other baseline methods and successfully models the relationship between the style distribution and scene enhancements. Thus, although only trained with a dataset of room images in single styles, the trained generator can generate mixed stylistic enhancements by specifying multiple styles as the condition. Our approach is the first to apply a Gumbel-Softmax module in conditional Wasserstein GANs, as well as the first to explore the application of GAN-based models in the scene enhancement field

A Differentiable Generative Adversarial Network for Open Domain Dialogue

Paper presented at the IWSDS 2019: International Workshop on Spoken Dialogue Systems Technology, Siracusa, Italy, April 24-26, 2019This work presents a novel methodology to train open domain neural dialogue systems within the framework of Generative Adversarial Networks with gradient-based optimization methods. We avoid the non-differentiability related to text-generating networks approximating the word vector corresponding to each generated token via a top-k softmax. We show that a weighted average of the word vectors of the most probable tokens computed from the probabilities resulting of the top-k softmax leads to a good approximation of the word vector of the generated token. Finally we demonstrate through a human evaluation process that training a neural dialogue system via adversarial learning with this method successfully discourages it from producing generic responses. Instead it tends to produce more informative and variate ones.This work has been partially funded by the Basque Government under grant PRE_2017_1_0357, by the University of the Basque Country UPV/EHU under grant PIF17/310, and by the H2020 RIA EMPATHIC (Grant N: 769872)