pdfcoffee

Generative Adversarial Networks
Cool applications of GANs
We have seen that the generator can learn how to forge data. This means that it
learns how to create new synthetic data that is created by the network that appears
to be authentic and human-made. Before going into the details of some GAN code,
we would like to share the results of a recent paper [6] (code is available online at
https://github.com/hanzhanggit/StackGAN) where a GAN has been used to
synthesize forged images starting from a text description. The results are impressive:
the first column is the real image in the test set and all the rest of the columns
are the images generated from the same text description by Stage-I and Stage-II
of StackGAN. More examples are available on YouTube (https://www.youtube.
com/watch?v=SuRyL5vhCIM&feature=youtu.be):
Figure 9: Image generation of birds, using GANs
Figure 10: Image generation of flowers, using GANs
Now let us see how a GAN can learn to "forge" the MNIST dataset. In this case
it is a combination of GAN and CNNs used for the generator and discriminator
networks. At the beginning the generator creates nothing understandable, but after
a few iterations synthetic forged numbers are progressively clearer and clearer. In
this image the panels are ordered by increasing training epochs and you can see the
quality improving among the panels:
[ 214 ]