  Generative Adversarial Networks (GANs) are powerful generative models that
achieved strong results, mainly in the image domain. However, the training of
GANs is not trivial, presenting some challenges tackled by different
strategies. Evolutionary algorithms, such as COEGAN, were recently proposed as
a solution to improve the GAN training, overcoming common problems that affect
the model, such as vanishing gradient and mode collapse. In this work, we
propose an evaluation method based on t-distributed Stochastic Neighbour
Embedding (t-SNE) to assess the progress of GANs and visualize the distribution
learned by generators in training. We propose the use of the feature space
extracted from trained discriminators to evaluate samples produced by
generators and from the input dataset. A metric based on the resulting t-SNE
maps and the Jaccard index is proposed to represent the model quality.
Experiments were conducted to assess the progress of GANs when trained using
COEGAN. The results show both by visual inspection and metrics that the
Evolutionary Algorithm gradually improves discriminators and generators through
generations, avoiding problems such as mode collapse.
