  Generative adversarial network (GAN) has gotten wide re-search interest in
the field of deep learning. Variations of GAN have achieved competitive results
on specific tasks. However, the stability of training and diversity of
generated instances are still worth studying further. Training of GAN can be
thought of as a greedy procedure, in which the generative net tries to make the
locally optimal choice (minimizing loss function of discriminator) in each
iteration. Unfortunately, this often makes generated data resemble only a few
modes of real data and rotate between modes. To alleviate these problems, we
propose a novel training strategy to restrict greed in training of GAN. With
help of our method, the generated samples can cover more instance modes with
more stable training process. Evaluating our method on several representative
datasets, we demonstrate superiority of improved training strategy on typical
GAN models with different distance metrics.
