MMGAN: Manifold Matching Generative Adversarial Network for Generating Images

Generative adversarial networks (GANs) are considered a new overarching paradigm in the world of generative models. However, it is well-known that GANs are difficult to train, and several different techniques have been proposed in order to stabilize their training. In this paper, we propose a novel training method called manifold matching, and a new GAN model called Manifold Matching GAN (MMGAN). In MMGAN, vector representations extracted from the last layer of the discriminator are used to train the generator. It finds two manifolds representing the vector representations of real and fake images. If these two manifolds match, it means that real and fake images are identical from the perspective of the discriminator because the manifolds are constructed from the discriminator’s last layer. In general, it is much easier to train the discriminator, and it becomes more accurate as epochs proceed. This implies that the manifold matching also becomes very accurate as the discriminator is trained. We also use the kernel trick to find a better manifold structure. We conduct in-depth experiments with three image datasets and show comparisons with several state-of-the-art GAN models. Our experiments demonstrate the efficacy of the proposed MMGAN model.