  A class of recent approaches for generating images, called Generative
Adversarial Networks (GAN), have been used to generate impressively realistic
images of objects, bedrooms, handwritten digits and a variety of other image
modalities. However, typical GAN-based approaches require large amounts of
training data to capture the diversity across the image modality. In this
paper, we propose DeLiGAN -- a novel GAN-based architecture for diverse and
limited training data scenarios. In our approach, we reparameterize the latent
generative space as a mixture model and learn the mixture model's parameters
along with those of GAN. This seemingly simple modification to the GAN
framework is surprisingly effective and results in models which enable
diversity in generated samples although trained with limited data. In our work,
we show that DeLiGAN can generate images of handwritten digits, objects and
hand-drawn sketches, all using limited amounts of data. To quantitatively
characterize intra-class diversity of generated samples, we also introduce a
modified version of "inception-score", a measure which has been found to
correlate well with human assessment of generated samples.
