Restoring Latent Vectors From Generative Adversarial Networks Using Genetic Algorithms

Rapid and massive advances in deep learning have made it possible to address with issues with computer vision. In recent years, one type of generative model has emerged, generative adversarial networks (GAN), that enables creating realistic and plausible images. GAN allows for building competition models based on game theory that allows for modeling data probability distributions. Since the introduction of GAN, researchers have conducted many follow-up studies to apply and improve these models. In this article, using a global optimization technique called a genetic algorithm, we suggest the methodology restoring latent vector of pre-trained GAN and measure its performance as hyper-parameters and fitness functions; specifically, we utilize the mating and mutation rate as hyper-parameters of the genetic algorithms and use the mean squared error and the structural similarity as the fitness functions and evaluate their impact. We obtain image reconstruction results through experiment using the MNIST, Fashion-MNIST, Cifar10, and CelebA dataset, and we compare our method with the gradient descent method. We discuss limitations of these experiments and future research topics.