Generating images with recurrent adversarial networksGatys et al. (2015) showed that optimizing pixels to match features in a convolutional network with respect reference image features is a way to render images of high visual quality. We show that unrolling this gradient-based optimization yields a recurrent computation that creates images by incrementally adding onto a visual "canvas". We propose a recurrent generative model inspired by this view,
Deeplearning 4 j のクイックスタートガイド - Deeplearning4j: Open-source, Distributed Deep Learning for the JVM
