Generating Large Images from Latent Vectors - Part Two

In a previous post, we've looked at a generative algorithm that can produce images of digits at arbitrary high resolutions, while training on on a set of low resolution images, such as MNIST or CIFAR-10. This post explores several changes to the previous model to produce more interesting results. Specifically, we removed the use of pixel-by-pixel reconstruction loss in the Variation Autoencoder. The discriminator network used to detect fake images is replaced by a classifier network. The generator network used previously had been a relatively large network consisting of 4 layers of 128 fully connected nodes, and we explore replacing this network with a much deeper network of 96 layers, but only with only 6 nodes in each layer.