  Recent research has shown that it is possible to find interpretable
directions in the latent spaces of pre-trained Generative Adversarial Networks
(GANs). These directions enable controllable image generation and support a
wide range of semantic editing operations, such as zoom or rotation. The
discovery of such directions is often done in a supervised or semi-supervised
manner and requires manual annotations which limits their use in practice. In
comparison, unsupervised discovery allows finding subtle directions that are
difficult to detect a priori. In this work, we propose a contrastive
learning-based approach to discover semantic directions in the latent space of
pre-trained GANs in a self-supervised manner. Our approach finds semantically
meaningful dimensions comparable with state-of-the-art methods.
