  Generative models have recently received renewed attention as a result of
adversarial learning. Generative adversarial networks consist of samples
generation model and a discrimination model able to distinguish between genuine
and synthetic samples. In combination with convolutional (for the
discriminator) and de-convolutional (for the generator) layers, they are
particularly suitable for image generation, especially of natural scenes.
However, the presence of fully connected layers adds global dependencies in the
generated images. This may lead to high and global variations in the generated
sample for small local variations in the input noise. In this work we propose
to use architec-tures based on fully convolutional networks (including among
others dilated layers), architectures specifically designed to generate
globally ergodic images, that is images without global dependencies. Conducted
experiments reveal that these architectures are well suited for generating
natural textures such as geologic structures .
