  The latent space of GANs contains rich semantics reflecting the training
data. Different methods propose to learn edits in latent space corresponding to
semantic attributes, thus allowing to modify generated images. Most supervised
methods rely on the guidance of classifiers to produce such edits. However,
classifiers can lead to out-of-distribution regions and be fooled by
adversarial samples. We propose an alternative formulation based on the
Wasserstein loss that avoids such problems, while maintaining performance
on-par with classifier-based approaches. We demonstrate the effectiveness of
our method on two datasets (digits and faces) using StyleGAN2.
