  In this paper, we introduce Random Path Generative Adversarial Network
(RPGAN) -- an alternative design of GANs that can serve as a tool for
generative model analysis. While the latent space of a typical GAN consists of
input vectors, randomly sampled from the standard Gaussian distribution, the
latent space of RPGAN consists of random paths in a generator network. As we
show, this design allows to understand factors of variation, captured by
different generator layers, providing their natural interpretability. With
experiments on standard benchmarks, we demonstrate that RPGAN reveals several
interesting insights about the roles that different layers play in the image
generation process. Aside from interpretability, the RPGAN model also provides
competitive generation quality and allows efficient incremental learning on new
data.
