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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.