  Generative adversarial networks (GANs) aim to generate realistic data from
some prior distribution (e.g., Gaussian noises). However, such prior
distribution is often independent of real data and thus may lose semantic
information (e.g., geometric structure or content in images) of data. In
practice, the semantic information might be represented by some latent
distribution learned from data, which, however, is hard to be used for sampling
in GANs. In this paper, rather than sampling from the pre-defined prior
distribution, we propose a Local Coordinate Coding (LCC) based sampling method
to improve GANs. We derive a generalization bound for LCC based GANs and prove
that a small dimensional input is sufficient to achieve good generalization.
Extensive experiments on various real-world datasets demonstrate the
effectiveness of the proposed method.
