  The latent spaces of GAN models often have semantically meaningful
directions. Moving in these directions corresponds to human-interpretable image
transformations, such as zooming or recoloring, enabling a more controllable
generation process. However, the discovery of such directions is currently
performed in a supervised manner, requiring human labels, pretrained models, or
some form of self-supervision. These requirements severely restrict a range of
directions existing approaches can discover. In this paper, we introduce an
unsupervised method to identify interpretable directions in the latent space of
a pretrained GAN model. By a simple model-agnostic procedure, we find
directions corresponding to sensible semantic manipulations without any form of
(self-)supervision. Furthermore, we reveal several non-trivial findings, which
would be difficult to obtain by existing methods, e.g., a direction
corresponding to background removal. As an immediate practical benefit of our
work, we show how to exploit this finding to achieve competitive performance
for weakly-supervised saliency detection.
