Advanced Deep Learning with Keras

Improved GANs
Since the introduction of the Generative Adversarial Networks (GANs) in 2014[1],
its popularity has rapidly increased. GANs have proved to be a useful generative
model that can synthesize new data that look real. Many of the research papers
in deep learning that followed, proposed measures to address the difficulties
and limitations of the original GAN.
As we discussed in previous chapters, GANs can be notoriously difficult to train
and prone to mode collapse. Mode collapse is a situation where the generator is
producing outputs that look the same even though the loss functions are already
optimized. In the context of MNIST digits, with mode collapse, the generator
may only be producing digits 4 and 9 since they look similar. Wasserstein GAN
(WGAN)[2] addressed these problems by arguing that stable training and mode
collapse can be avoided by simply replacing the GAN loss function based on
Wasserstein 1 or Earth-Mover distance (EMD).
However, the issue of stability is not the only problem of GANs. There is also
the increasing need to improve the perceptive quality of the generated images.
Least Squares GAN (LSGAN)[3] proposed to address both these problems
simultaneously. The basic premise is that sigmoid cross entropy loss leads to
a vanishing gradient during training. This results in poor image quality. Least
squares loss does not induce vanishing gradients. The resulting generated images
are of higher perceptive quality when compared to vanilla GAN generated images.
In the previous chapter, CGAN introduced a method for conditioning the output
of the generator. For example, if we wanted to get digit 8, we would include the
conditioning label in the input to the generator. Inspired by CGAN, the Auxiliary
Classifier GAN (ACGAN)[4] proposed a modified conditional algorithm that
results in better perceptive quality and diversity of the outputs.
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