Advanced Deep Learning with Keras

It is straightforward to implement β -VAE. For example, for the CVAE from the
previous, the required modification is the extra beta factor in kl_loss.
kl_loss = 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var)
kl_loss = K.sum(kl_loss, axis=-1)
kl_loss *= -0.5 * beta
Chapter 8
CVAE is a special case of β -VAE with β = 1. Everything else is the same. However,
determining the value of β requires some trial and error. There must be a careful
balance between the reconstruction error and regularization for latent codes
independence. The disentanglement is maximized at around . When the
value of β > 8 , the β -VAE is forced to learn one disentangled representation only
while muting the other latent dimension:
Figure 8.3.1: The latent vector mean values for the test dataset ( β -VAE with 7 β = )
Color images can be found on the book GitHub repository: https://github.com/PacktPublishing/Advanced-
Deep-Learning-with-Keras/tree/master/chapter8-vae.
[ 265 ]