Advanced Deep Learning with Keras

Generative Adversarial Networks (GANs)
Conclusion
This chapter discussed the general principles behind GANs, to give you a foundation
to the more advanced topics we'll now move on to, including Improved GANs,
Disentangled Representations GANs, and Cross-Doman GANs. We started this
chapter by understanding how GANs are made up of two networks called generator
and discriminator. The role of the discriminator is to discriminate between real
and fake signals. The aim of the generator is to fool the discriminator. The generator
is normally combined with the discriminator to form an adversarial network. It is
through training the adversarial network that the generator learns how to produce
fake signals that can trick the discriminator.
We also learned how GANs are easy to build but notoriously difficult to train.
Two example implementations in Keras were presented. DCGAN demonstrated
that it is possible to train GANs to generate fake images using deep CNNs. The
fake images are MNIST digits. However, the DCGAN generator has no control over
which specific digit it should draw. CGAN addressed this problem by conditioning
the generator to draw a specific digit. The condition is in the form of a one-hot label.
CGAN is useful if we want to build an agent that can generate data of a specific class.
In the next chapter, improvements on the DCGAN and CGAN will be introduced.
In particular, the focus is on how to stabilize the training of DCGAN and how to
improve the perceptive quality of CGAN. This will be done by introducing new
loss functions and slightly different model architectures.
References
1. Ian Goodfellow. NIPS 2016 Tutorial: Generative Adversarial Networks. arXiv
preprint arXiv:1701.00160, 2016 (https://arxiv.org/pdf/1701.00160.
pdf).
2. Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised Representation
Learning with Deep Convolutional Generative Adversarial Networks. arXiv
preprint arXiv:1511.06434, 2015 (https://arxiv.org/pdf/1511.06434.
pdf).
3. Mehdi Mirza and Simon Osindero. Conditional Generative Adversarial Nets.
arXiv preprint arXiv:1411.1784, 2014 (https://arxiv.org/pdf/1411.1784.
pdf).
4. Tero Karras and others. Progressive Growing of GANs for Improved Quality,
Stability, and Variation. ICLR, 2018 (https://arxiv.org/pdf/1710.10196.
pdf).
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