Aaaaand… finally; after Parts 1-4 we are ready to implement and test the pixelSNAIL model. In the previous posts we went in detail through the theory and concepts behind the pixelCNN style models which were designed to generate unique new images after being trained on a dataset. In this post I will go through some runs of the pixelSNAIL model; using a Keras implementation I built to port the original code from the largely deprecated TensorFlow 1 to TensorFlow 2. You can find this repository here. \begin{aligned} \end{aligned} Initial Exploration with pixelSNAIL The first task was to get pixelSNAIL working; this code is very difficult to read and also is written in TensorFlow 1 style (I think TensorFlow 1 tends[…]
Tag: pixelCNN
Hello and welcome to Part 4 in the series on autoregressive generative models. We have covered some of the theory of these models and have implemented causality and probability to allow a CNN to be capable of generating new images based on a training set. In this post we are going to focus on the building blocks that are used to construct the leading model of this class; pixelSNAIL (as well as some of the other variants of pixelCNNs). This brings in quite a few ideas from the field of deep learning and in some cases I will touch on them only briefly; and aim to cover them in more detail in other posts. Gated Residual Block We have discussed[…]
Hello and welcome to this series of tutorials on autoregressive models! I decided to put this together to help me (and hopefully you) learn about these types of model which have cutting edge applications in image generation. I want to go into depth on this because there are some core ideas I think it’s worth exploring and understanding; particularly as I find the papers are very thin on detail. I have split this up into chunks Part 1 : General introduction to generative autoregressive models and particularly the pixelCNN family of models. Part 2 : Theory (Probability); an exploration/explanation of the key ideas that make these models work. Part 3 : Theory (Causality); an exploration/explanation of the key ideas that[…]