PixelCNNs are a recently proposed class of powerful generative models with tractable likelihood. Here we discuss our implementation of PixelCNNs which we make available at https://github.com/openai/pixel-cnn. Our implementation contains a number of modifications to the original model that both simplify its structure and improve its performance. 1) We use a discretized logistic mixture likelihood on the pixels, rather than a 256-way softmax, which we find to speed up training. 2) We condition on whole pixels, rather than R/G/B sub-pixels, simplifying the model structure. 3) We use downsampling to efficiently capture structure at multiple resolutions. 4) We introduce additional short-cut connections to further speed up optimization. 5) We regularize the model using dropout. Finally, we present state-of-the-art log likelihood results on CIFAR-10 to demonstrate the usefulness of these modifications.
@article{arxiv.1701.05517,
title = {PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications},
author = {Tim Salimans and Andrej Karpathy and Xi Chen and Diederik P. Kingma},
journal= {arXiv preprint arXiv:1701.05517},
year = {2017}
}