In this article, we discuss the CycleGAN architecture. Here we discuss the CycleGAN architecture and explain how each architectural component can be implemented. Find the whole articles of this series on my Medium Profile : CycleGAN Series ¨ Introduction In this series of articles, we’ll present a Mobile Image-to-Image Translation system based on a Cycle-Consistent Adversarial Networks (CycleGAN) . We’ll build a CycleGAN that can perform unpaired image-to-image translation, as well as show you some entertaining yet academically deep examples. We’ll also discuss how such a trained network, built with TensorFlow and Keras, can be converted to TensorFlow Lite and used as an app on mobile devices. We assume that you are familiar with the concepts of Deep Learning, as well as with Jupyter Notebooks and TensorFlow. You are welcome to download the project code. In the previous article of this series , we discussed the concepts of conditional generative adversarial networks (CGAN). In this