  Generative models are known to be difficult to assess. Recent works,
especially on generative adversarial networks (GANs), produce good visual
samples of varied categories of images. However, the validation of their
quality is still difficult to define and there is no existing agreement on the
best evaluation process. This paper aims at making a step toward an objective
evaluation process for generative models. It presents a new method to assess a
trained generative model by evaluating the test accuracy of a classifier
trained with generated data. The test set is composed of real images.
Therefore, The classifier accuracy is used as a proxy to evaluate if the
generative model fit the true data distribution. By comparing results with
different generated datasets we are able to classify and compare generative
models. The motivation of this approach is also to evaluate if generative
models can help discriminative neural networks to learn, i.e., measure if
training on generated data is able to make a model successful at testing on
real settings. Our experiments compare different generators from the
Variational Auto-Encoders (VAE) and Generative Adversarial Network (GAN)
frameworks on MNIST and fashion MNIST datasets. Our results show that none of
the generative models is able to replace completely true data to train a
discriminative model. But they also show that the initial GAN and WGAN are the
best choices to generate on MNIST database (Modified National Institute of
Standards and Technology database) and fashion MNIST database.
