“PixelCNN++: Improving the PixelCNN With Discretized Logistic Mixture Likelihood and Other Modifications”, 2017-01-19 ():
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 Github.
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.