PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications

# PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications Source: [https://openai.com/index/pixelcnn-plus-plus/](https://openai.com/index/pixelcnn-plus-plus/) ## Abstract 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[this https URL⁠\(opens in a new window\)](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\.