  We propose a new architecture and training methodology for generative
adversarial networks. Current approaches attempt to learn the transformation
from a noise sample to a generated data sample in one shot. Our proposed
generator architecture, called $\textit{ChainGAN}$, uses a two-step process. It
first attempts to transform a noise vector into a crude sample, similar to a
traditional generator. Next, a chain of networks, called $\textit{editors}$,
attempt to sequentially enhance this sample. We train each of these units
independently, instead of with end-to-end backpropagation on the entire chain.
Our model is robust, efficient, and flexible as we can apply it to various
network architectures. We provide rationale for our choices and experimentally
evaluate our model, achieving competitive results on several datasets.
