  Numerous research efforts have been made to stabilize the training of the
Generative Adversarial Networks (GANs), such as through regularization and
architecture design. However, we identify the instability can also arise from
the fragile balance at the early stage of adversarial learning. This paper
proposes the CoopInit, a simple yet effective cooperative learning-based
initialization strategy that can quickly learn a good starting point for GANs,
with a very small computation overhead during training. The proposed algorithm
consists of two learning stages: (i) Cooperative initialization stage: The
discriminator of GAN is treated as an energy-based model (EBM) and is optimized
via maximum likelihood estimation (MLE), with the help of the GAN's generator
to provide synthetic data to approximate the learning gradients. The EBM also
guides the MLE learning of the generator via MCMC teaching; (ii) Adversarial
finalization stage: After a few iterations of initialization, the algorithm
seamlessly transits to the regular mini-max adversarial training until
convergence. The motivation is that the MLE-based initialization stage drives
the model towards mode coverage, which is helpful in alleviating the issue of
mode dropping during the adversarial learning stage. We demonstrate the
effectiveness of the proposed approach on image generation and one-sided
unpaired image-to-image translation tasks through extensive experiments.
