【论文】（论文+代码）加速的PixelCNN ++，图像生成效率提升了183倍

论文《FAST GENERATION FOR CONVOLUTIONAL AUTORGRESSIVE MODELS》摘要：

Convolutional autoregressive models have recently demonstrated state-of-the-art performance on a number of generation tasks. While fast, parallel training methods have been crucial for their success, generation is typically implemented in a näıve fashion where redundant computations are unnecessarily repeated. This results in slow generation, making such models infeasible for production environments. In this work, we describe a method to speed up generation in convolutional autoregressive models. The key idea is to cache hidden states to avoid redundant computation. We apply our fast generation method to the Wavenet and PixelCNN++ models and achieve up to 21x and 183x speedups respectively.

论文链接：

https://openreview.net/pdf?id=rkdF0ZNKl

代码链接：

https://github.com/PrajitR/fast-pixel-cnn

原文链接：

http://weibo.com/5501429448/EwI42zZzt?type=comment#_rnd1487752595359