Augmenting GAN with continuous depth Neural ODE

Abstract

Generative adversarial networks are extremely powerful tools for generative modeling of complex data distributions. Research is being actively conducted towards further improving them as well as making their training easier and more stable. In this thesis, we present Neural ODE Generative Adversarial Network (NGAN), a framework that uses Neural ODE blocks instead of the standard convolutional neural networks (CNNs) as discriminators and generators within the generative adversarial network (GAN) setting. We show that NGAN outperforms convolutional-GAN at modeling image data distribution on MNIST dataset, evaluated on the generative adversarial metric. iii