Experimental Setup: PGGANs

Discover how to implement the progressive growing methodology of GANs, covering training techniques and supporting functions.

We'll cover the following

Training

In this lesson, we are going to implement training functions that are compatible with the PGGAN methodology. These training and logging functions will be used to train the discriminator and the generator, which will also be implemented in this lesson.

We will also implement helper functions in order to make it easier to compute our loss function based on the WGAN-GP.

Training

Our training method consists of the following two parts:

Initializing the required variables and compiling models
The training loop itself

We will start by looking at the training layout:

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Getting Started

Deep Learning Basics and Environment Test

Introduction to Generative Models

Implementing Our First GAN

Evaluating Our First GAN

Improving Our First GAN

Synthesizing and Manipulating Images with GANs

Progressive Growing of GANs

Generation of Discrete Sequences Using GANs

Text-to-Image Synthesis with GANs

Speech Enhancement with GANs

TequilaGAN—Identifying GAN Samples

What’s Next in GANs

Conclusion

Appendix

Experimental Setup: PGGANs

Training