Advanced Deep Learning with Keras

Cross-Domain GANs
In the case of PatchGAN, the output 1 is understandable given the predicted MNIST
digit is constrained to one digit. There are somehow correct predictions like in 2 nd
row last 3 columns of the SVHN digits, 6, 3, and 4 are converted to 6, 3, and 6 by
CycleGAN without PatchGAN. However, the outputs on both flavors of CycleGAN
are consistently single digit and recognizable.
The problem exhibited in the conversion from MNIST to SVHN where a digit in
the source domain is translated to another digit in the target domain is called label
flipping [8]. Although the predictions of CycleGAN are cycle-consistent, they are
not necessarily semantic consistent. The meaning of digits is lost during translation.
To address this problem, Hoffman [8] introduced an improved CycleGAN called
CyCADA (Cycle-Consistent Adversarial Domain Adaptation). The difference is the
additional semantic loss term ensures that the prediction is not only cycle-consistent
but also sematic-consistent:
Figure 7.1.12: Style transfer of test data from the MNIST domain to SVHN. Original color photo can be found
on the book GitHub repository, https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/
blob/master/chapter7-cross-domain-gan/README.md.
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