Chapter 17: Generative Adversarial Networks for Synthesizing New Data

Chapter Outline

• Introducing generative adversarial networks

  • Starting with autoencoders

  • Generative models for synthesizing new data

  • Generating new samples with GANs

  • Understanding the loss functions for the generator and discriminator networks in a GAN model

• Implementing a GAN from scratch

  • Training GAN models on Google Colab

  • Implementing the generator and the discriminator networks

  • Defining the training dataset

  • Training the GAN model

• Improving the quality of synthesized images using a convolutional and Wasserstein GAN

  • Transposed convolution

  • Batch normalization

  • Implementing the generator and discriminator

  • Dissimilarity measures between two distributions

  • Using EM distance in practice for GANs

  • Gradient penalty

  • Implementing WGAN-GP to train the DCGAN model

  • Mode collapse

  • Other GAN applications

• Summary

Please refer to the README.md file in ../ch01 for more information about running the code examples.