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We present a neural network architecture based upon the Autoencoder (AE) andGenerative Adversarial Network (GAN) that promotes a convex latent distributionby training adversarially on latent space interpolations. By using an AE asboth the generator and discriminator of a GAN, we pass a pixel-wise errorfunction across the discriminator, yielding an AE which produces non-blurrysamples that match both high- and low-level features of the original images.Interpolations between images in this space remain within the latent-spacedistribution of real images as trained by the discriminator, and therforepreserve realistic resemblances to the network inputs. Code available athttps://github.com/timsainb/GAIA

Generative adversarial interpolative autoencoding: adversarial training on latent space interpolations encourage convex latent distributions