| Title | Learning a Deep Reinforcement Learning Policy Over the Latent Space of a Pre-trained GAN for Semantic Age Manipulation |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Shubham, Kumar, Venkatesh, Gopalakrishnan, Sachdev, Reijul, Akshi, Jayagopi, Dinesh Babu, Srinivasaraghavan, G. |
| Conference Name | 2021 International Joint Conference on Neural Networks (IJCNN) |
| Date Published | jul |
| Keywords | Computational modeling, Computer architecture, Generative Adversarial Learning, generative adversarial networks, Markov processes, Metrics, Neural networks, pubcrawl, reinforcement learning, resilience, Resiliency, Scalability, Semantics |
| Abstract | 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. |
| DOI | 10.1109/IJCNN52387.2021.9533685 |
| Citation Key | shubham_learning_2021 |
Learning a Deep Reinforcement Learning Policy Over the Latent Space of a Pre-trained GAN for Semantic Age Manipulation