  Two-dimensional array-based datasets are pervasive in a variety of domains.
Current approaches for generative modeling have typically been limited to
conventional image datasets and performed in the pixel domain which do not
explicitly capture the correlation between pixels. Additionally, these
approaches do not extend to scientific and other applications where each
element value is continuous and is not limited to a fixed range. In this paper,
we propose a novel approach for generating two-dimensional datasets by moving
the computations to the space of representation bases and show its usefulness
for two different datasets, one from imaging and another from scientific
computing. The proposed approach is general and can be applied to any dataset,
representation basis, or generative model. We provide a comprehensive
performance comparison of various combinations of generative models and
representation basis spaces. We also propose a new evaluation metric which
captures the deficiency of generating images in pixel space.
