Advanced Deep Learning with Keras

Improved GANs
Conclusion
In this chapter, we've presented various improvements in the original algorithm
of GAN, first introduced in the previous chapter. WGAN proposed an algorithm
to improve the stability of training by using the EMD or Wassertein 1 loss. LSGAN
argued that the original cross-entropy function of GAN is prone to vanishing
gradients, unlike least squares loss. LSGAN proposed an algorithm to achieve stable
training and quality outputs. ACGAN convincingly improved the quality of the
conditional generation of MNIST digits by requiring the discriminator to perform
classification task on top of determining whether the input image is fake or real.
In the next chapter, we'll study how to control the attributes of generator outputs.
Whilst CGAN and ACGAN are able to indicate the desired digits to produce; we
have not analyzed GANs that can specify the attributes of outputs. For example,
we may want to control the writing style of the MNIST digits such as roundness,
tilt angle, and thickness. Therefore, the goal will be to introduce GANs with
disentangled representations to control the specific attributes of the generator
outputs.
References
1. Ian Goodfellow and others. Generative Adversarial Nets. Advances in
neural information processing systems, 2014(http://papers.nips.cc/
paper/5423-generative-adversarial-nets.pdf).
2. Martin Arjovsky, Soumith Chintala, and Léon Bottou, Wasserstein GAN. arXiv
preprint, 2017(https://arxiv.org/pdf/1701.07875.pdf).
3. Xudong Mao and others. Least Squares Generative Adversarial Networks.
2017 IEEE International Conference on Computer Vision (ICCV). IEEE
2017(http://openaccess.thecvf.com/content_ICCV_2017/papers/Mao_
Least_Squares_Generative_ICCV_2017_paper.pdf).
4. Augustus Odena, Christopher Olah, and Jonathon Shlens. Conditional Image
Synthesis with Auxiliary Classifier GANs. ICML, 2017(http://proceedings.
mlr.press/v70/odena17a/odena17a.pdf).
[ 160 ]