The same ideas used for forging MNIST images can be applied to other image domains. In this recipe, you will learn how to use the package located at https://github.com/carpedm20/DCGAN-tensorflow to train a DCGAN model on different datasets. The work is based on the paper Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, Alec Radford, Luke Metz, Soumith Chintal, 2015. Quoting the abstract:
In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attention. In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised learning. We introduce a class of CNNs called deep convolutional generative...
