Mobile Style Transfer With Image-to-Image Translation
In this article, we discuss the concepts of conditional generative adversarial networks (CGAN).
Here we do a brief overview of image-to-image translation and generative adversarial learning.
This is a series of articles discussing Image-to-Image Translation using CycleGAN. Find the Next article here.
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.
In this project, we’ll use:
- Jupyter Notebook as the IDE
- Libraries:
- TensorFlow 2.0
- NumPy
- MatplotLib
- Downloadable public CycleGAN datasets
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.
Image-to-Image Translation
Style transfer is built using image-to-image translation. This technique transfers images from source domain A to target domain B. What does that mean, exactly? To put it succinctly, image-to-image translation lets us take properties from one image…