October 2018
Intermediate to advanced
368 pages
9h 20m
English
Content preview from Advanced Deep Learning with KerasBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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StackedGAN
In the same spirit as InfoGAN, StackedGAN proposes a method for disentangling latent representations for conditioning generator outputs. However, StackedGAN uses a different approach to the problem. Instead of learning how to condition the noise to produce the desired output, StackedGAN breaks down a GAN into a stack of GANs. Each GAN is trained independently in the usual discriminator-adversarial manner with its own latent code.
Figure 6.2.1 shows us how StackedGAN works in the context of the hypothetical celebrity face generation. Assuming that the Encoder network is trained to classify celebrity faces.
The Encoder network is made of a stack of simple encoders, Encoder i where i = 0 … n - 1 corresponding to n features. Each encoder ...
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