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