  Truncation is widely used in generative models for improving the quality of
the generated samples, at the expense of reducing their diversity. We propose
to leverage the StyleGAN generative architecture to devise a new truncation
technique, based on a decomposition of the latent space into clusters, enabling
customized truncation to be performed at multiple semantic levels. We do so by
learning to re-generate W-space, the extended intermediate latent space of
StyleGAN, using a learnable mixture of Gaussians, while simultaneously training
a classifier to identify, for each latent vector, the cluster that it belongs
to. The resulting truncation scheme is more faithful to the original
untruncated samples and allows a better trade-off between quality and
diversity. We compare our method to other truncation approaches for StyleGAN,
both qualitatively and quantitatively.
