Advanced Deep Learning with Keras
Chapter 5Figure 5.3.4: A side by side comparison of outputs of CGAN and ACGAN conditioned with digits 0 to 9[ 159 ]
Improved GANsConclusionIn this chapter, we've presented various improvements in the original algorithmof GAN, first introduced in the previous chapter. WGAN proposed an algorithmto improve the stability of training by using the EMD or Wassertein 1 loss. LSGANargued that the original cross-entropy function of GAN is prone to vanishinggradients, unlike least squares loss. LSGAN proposed an algorithm to achieve stabletraining and quality outputs. ACGAN convincingly improved the quality of theconditional generation of MNIST digits by requiring the discriminator to performclassification 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; wehave 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 withdisentangled representations to control the specific attributes of the generatoroutputs.References1. Ian Goodfellow and others. Generative Adversarial Nets. Advances inneural information processing systems, 2014(http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf).2. Martin Arjovsky, Soumith Chintala, and Léon Bottou, Wasserstein GAN. arXivpreprint, 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). IEEE2017(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 ImageSynthesis with Auxiliary Classifier GANs. ICML, 2017(http://proceedings.mlr.press/v70/odena17a/odena17a.pdf).[ 160 ]
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Chapter 5
Figure 5.3.4: A side by side comparison of outputs of CGAN and ACGAN conditioned with digits 0 to 9
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