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Learning a disentangled representation of the latent space has become one ofthe most fundamental problems studied in computer vision. Recently, manyGenerative Adversarial Networks (GANs) have shown promising results ingenerating high fidelity images. However, studies to understand the semanticlayout of the latent space of pre-trained models are still limited. Severalworks train conditional GANs to generate faces with required semanticattributes. Unfortunately, in these attempts, the generated output is often notas photo-realistic as the unconditional state-of-the-art models. Besides, theyalso require large computational resources and specific datasets to generatehigh 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 conditionalpolicy for semantic manipulation along specific attributes under definedidentity bounds. Further, we have defined a semantic age manipulation schemeusing a locally linear approximation over the latent space. Results show thatour learned policy samples high fidelity images with required age alterations,while preserving the identity of the person.

Learning a Deep Reinforcement Learning Policy Over the Latent Space of a Pre-trained GAN for Semantic Age Manipulation