  In this paper we introduce a generative parametric model capable of producing
high quality samples of natural images. Our approach uses a cascade of
convolutional networks within a Laplacian pyramid framework to generate images
in a coarse-to-fine fashion. At each level of the pyramid, a separate
generative convnet model is trained using the Generative Adversarial Nets (GAN)
approach (Goodfellow et al.). Samples drawn from our model are of significantly
higher quality than alternate approaches. In a quantitative assessment by human
evaluators, our CIFAR10 samples were mistaken for real images around 40% of the
time, compared to 10% for samples drawn from a GAN baseline model. We also show
samples from models trained on the higher resolution images of the LSUN scene
dataset.
