Conditional GANs (cGANs) are an extension of the GAN model. They allow for the generation of images that have certain conditions or attributes and have proved to be better than vanilla GANs as a result. In this chapter, we will implement a cGAN that, once trained, can perform automatic face aging. The cGAN network that we will implement was first introduced by Grigory Antipov, Moez Baccouche, and Jean-Luc Dugelay, in their paper titled Face Aging With Conditional Generative Adversarial Networks, which can be found at the following link: https://arxiv.org/pdf/1702.01983.pdf.
In this chapter, we will cover the following topics:
- Introducing cGANs for face aging
- Setting up the project
- Preparing the data
- A Keras implementation of a cGAN
- Training a cGAN
- Evaluation and hyperparameter tuning
- Practical applications of face aging
