  We propose a discrete latent distribution for Generative Adversarial Networks
(GANs). Instead of drawing latent vectors from a continuous prior, we sample
from a finite set of learnable latents. However, a direct parametrization of
such a distribution leads to an intractable linear increase in memory in order
to ensure sufficient sample diversity. We address this key issue by taking
inspiration from the encoding of information in biological organisms. Instead
of learning a separate latent vector for each sample, we split the latent space
into a set of genes. For each gene, we train a small bank of gene variants.
Thus, by independently sampling a variant for each gene and combining them into
the final latent vector, our approach can represent a vast number of unique
latent samples from a compact set of learnable parameters. Interestingly, our
gene-inspired latent encoding allows for new and intuitive approaches to
latent-space exploration, enabling conditional sampling from our
unconditionally trained model. Moreover, our approach preserves
state-of-the-art photo-realism while achieving better disentanglement than the
widely-used StyleMapping network.
