  In autoencoder, the encoder generally approximates the latent distribution
over the dataset, and the decoder generates samples using this learned latent
distribution. There is very little control over the latent vector as using the
random latent vector for generation will lead to trivial outputs. This work
tries to address this issue by using the LatentGAN generator to directly learn
to approximate the latent distribution of the autoencoder and show meaningful
results on MNIST, 3D Chair, and CelebA datasets, an additional
information-theoretic constrain is used which successfully learns to control
autoencoder latent distribution. With this, our model also achieves an error
rate of 2.38 on MNIST unsupervised image classification, which is better as
compared to InfoGAN and AAE.
