Title: Generative adversarial interpolative autoencoding: adversarial training on latent space interpolations encourage convex latent distributions,
Abstract: We present a neural network architecture based upon the Autoencoder (AE) and
Generative Adversarial Network (GAN) that promotes a convex latent distribution
by training adversarially on latent space interpolations. By using an AE as
both the generator and discriminator of a GAN, we pass a pixel-wise error
function across the discriminator, yielding an AE which produces non-blurry
samples that match both high- and low-level features of the original images.
Interpolations between images in this space remain within the latent-space
distribution of real images as trained by the discriminator, and therfore
preserve realistic resemblances to the network inputs.