In recent times, many of the breakthroughs in various vision-related tasks
have revolved around improving learning of deep models; these methods have
ranged from network architectural improvements such as Residual Networks, to
various forms of regularisation such as Batch Normalisation. In essence, many
of these techniques revolve around better conditioning, allowing for deeper and
deeper models to be successfully learned...
In this paper, we look towards better
conditioning Generative Adversarial Networks (GANs) in an unsupervised learning
setting. Our method embeds the powerful discriminating capabilities of a
decision forest into the discriminator of a GAN. This results in a better
conditioned model which learns in an extremely stable way. We demonstrate
empirical results which show both clear qualitative and quantitative evidence
of the effectiveness of our approach, gaining significant performance
improvements over several popular GAN-based approaches on the Oxford Flowers
and Aligned Celebrity Faces datasets.