  Given a dataset of images containing different objects with different
features such as shape, size, rotation, and x-y position; and a Variational
Autoencoder (VAE); creating a disentangled encoding of these features in the
hidden space vector of the VAE was the task of interest in this paper. The
dSprite dataset provided the desired features for the required experiments in
this research. After training the VAE combined with a Generative Adversarial
Network (GAN), each dimension of the hidden vector was disrupted to explore the
disentanglement in each dimension. Note that the GAN was used to improve the
quality of output image reconstruction.
