Advanced Deep Learning with Keras

Variational Autoencoders (VAEs)
Preceding figure shows the continuous latent space of a VAE using the CNN
implementation after 30 epochs. The region where each digit is assigned may
be different, but the distribution is roughly the same. Following figure shows
us the output of the generative model. Qualitatively, there are fewer digits that
are ambiguous as compared to Figure 8.1.7 with the MLP implementation:
Figure 8.1.12: The digits generated as a function of latent vector mean values (VAE CNN).
For ease of interpretation, the range of values for the mean is similar to Figure 8.1.11.
Conditional VAE (CVAE)
Conditional VAE [2] is similar to the idea of CGAN. In the context of the MNIST
dataset, if the latent space is randomly sampled, VAE has no control over which
digit will be generated. CVAE is able to address this problem by including
a condition (a one-hot label) of the digit to produce. The condition is imposed
on both the encoder and decoder inputs.
Formally, the core equation of VAE in Equation 8.1.10 is modified to include the
condition c:
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