  Learning a disentangled representation of the latent space has become one of
the most fundamental problems studied in computer vision. Recently, many
Generative Adversarial Networks (GANs) have shown promising results in
generating high fidelity images. However, studies to understand the semantic
layout of the latent space of pre-trained models are still limited. Several
works train conditional GANs to generate faces with required semantic
attributes. Unfortunately, in these attempts, the generated output is often not
as photo-realistic as the unconditional state-of-the-art models. Besides, they
also require large computational resources and specific datasets to generate
high fidelity images. In our work, we have formulated a Markov Decision Process
(MDP) over the latent space of a pre-trained GAN model to learn a conditional
policy for semantic manipulation along specific attributes under defined
identity bounds. Further, we have defined a semantic age manipulation scheme
using a locally linear approximation over the latent space. Results show that
our learned policy samples high fidelity images with required age alterations,
while preserving the identity of the person.
