Figure 1.

A schematic of the VAE architecture. Input allele counts are passed to an encoder network which outputs parameters describing a sample’s location as a multivariate normal in latent space. Samples from this distribution are then passed to a decoder network which generates a new genotype vector. The loss function used to update weights and biases of both networks is the sum of reconstruction error (from comparing true and generated genotypes) and KL divergence between sample latent distributions and $\mathcal{N}(0,1)$.