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.
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