  Recent research has shown great potential for finding interpretable
directions in the latent spaces of pre-trained Generative Adversarial Networks
(GANs). These directions provide controllable generation and support a wide
range of semantic editing operations such as zoom or rotation. The discovery of
such directions is often performed in a supervised or semi-supervised fashion
and requires manual annotations, limiting their applications in practice. In
comparison, unsupervised discovery enables finding subtle directions a priori
hard to recognize. In this work, we propose a contrastive-learning-based
approach for discovering semantic directions in the latent space of pretrained
GANs in a self-supervised manner. Our approach finds semantically meaningful
dimensions compatible with state-of-the-art methods.
