  Recent deep generative models are able to provide photo-realistic images as
well as visual or textual content embeddings useful to address various tasks of
computer vision and natural language processing. Their usefulness is
nevertheless often limited by the lack of control over the generative process
or the poor understanding of the learned representation. To overcome these
major issues, very recent work has shown the interest of studying the semantics
of the latent space of generative models. In this paper, we propose to advance
on the interpretability of the latent space of generative models by introducing
a new method to find meaningful directions in the latent space of any
generative model along which we can move to control precisely specific
properties of the generated image like the position or scale of the object in
the image. Our method does not require human annotations and is particularly
well suited for the search of directions encoding simple transformations of the
generated image, such as translation, zoom or color variations. We demonstrate
the effectiveness of our method qualitatively and quantitatively, both for GANs
and variational auto-encoders.
