In this paper we propose Spatial PixelCNN, a conditional autoregressive model that generates images from small patches. By conditioning on a grid of pixel coordinates and global features extracted from a Variational Autoencoder (VAE), we are able to train on patches of images, and reproduce the full-sized image. We show that it not only allows for generating high quality samples at the same resolution as the underlying dataset, but is also capable of upscaling images to arbitrary resolutions (tested at resolutions up to @math ) on the MNIST dataset. Compared to a PixelCNN++ baseline, Spatial PixelCNN quantitatively and qualitatively achieves similar performance on the MNIST dataset.
Picbreeder is an online service that allows users to collaboratively evolve images. Like in other Interactive Evolutionary Computation (IEC) programs, users evolve images on Picbreeder by selecting ones that appeal to them to produce a new generation. However, Picbreeder also offers an online community in which to share these images, and most importantly, the ability to continue evolving others' images. Through this process of branching from other images, and through continually increasing image complexity made possible by the NeuroEvolution of Augmenting Topologies (NEAT) algorithm, evolved images proliferate unlike in any other current IEC systems. Participation requires no explicit talent from the users, thereby opening Picbreeder to the entire Internet community. This paper details how Picbreeder encourages innovation, featuring images that were collaboratively evolved.