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Chapter 6
Figure 7: The architecture of the InfoGAN, visualized
The concatenated vector (Z,c) is fed to the generator. Q(c|X) is also a neural network.
Combined with the generator it works to form a mapping between random noise Z
and its latent code c_hat. It aims to estimate c given X. This is achieved by adding a
regularization term to the objective function of the conventional GAN:
mmmmmm DD mmmmmm GG VV 1 (DD, GG) = VV GG (DD, GG) − λλ1(c; G(Z, c))
The term V G
(D,G) is the loss function of the conventional GAN, and the second term
is the regularization term, where λλ is a constant. Its value was set to 1 in the paper,
and I(c;G(Z,c)) is the mutual information between the latent code c and the generatorgenerated
image G(Z,c).
Following are the exciting results of the InfoGAN on the MNIST dataset:
Figure 8: Results of using the InfoGAN on the MNIST dataset
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