  As recent generative models can generate photo-realistic images, people seek
to understand the mechanism behind the generation process. Interpretable
generation process is beneficial to various image editing applications. In this
work, we propose a framework to discover interpretable directions in the latent
space given arbitrary pre-trained generative adversarial networks. We propose
to learn the transformation from prior one-hot vectors representing different
attributes to the latent space used by pre-trained models. Furthermore, we
apply a centroid loss function to improve consistency and smoothness while
traversing through different directions. We demonstrate the efficacy of the
proposed framework on a wide range of datasets. The discovered direction
vectors are shown to be visually corresponding to various distinct attributes
and thus enable attribute editing.
