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 [10] . 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.
The LAPGAN REF model generates images and improves their quality from coarse to fine ones by using a cascade convolution network within the Laplacian pyramid framework.
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
ArXiv
ArXiv
Computer Science