-VAE: VAE with disentangled latent representations

In Chapter 6, Disentangled Representation GANs, the concept, and importance of the disentangled representation of latent codes were discussed. We can recall that a disentangled representation is where single latent units are sensitive to changes in single generative factors while being relatively invariant to changes in other factors [3]. Varying a latent code results to changes in one attribute of the generated output while the rest of the properties remain the same.

In the same chapter, InfoGANs [4] demonstrated to us that in the MNIST dataset, it is possible to control which digit to generate and the tilt and thickness of writing style. Observing the results in the previous section, it can be noticed that the VAE is intrinsically disentangling the latent vector dimensions to a certain extent. For example, looking at digit 8 in Figure 8.2.6, navigating z[1] from top to bottom decreases the width and roundness...