  In conditional Generative Adversarial Networks (cGANs), when two different
initial noises are concatenated with the same conditional information, the
distance between their outputs is relatively smaller, which makes minor modes
likely to collapse into large modes. To prevent this happen, we proposed a
hierarchical mode exploring method to alleviate mode collapse in cGANs by
introducing a diversity measurement into the objective function as the
regularization term. We also introduced the Expected Ratios of Expansion (ERE)
into the regularization term, by minimizing the sum of differences between the
real change of distance and ERE, we can control the diversity of generated
images w.r.t specific-level features. We validated the proposed algorithm on
four conditional image synthesis tasks including categorical generation, paired
and un-paired image translation and text-to-image generation. Both qualitative
and quantitative results show that the proposed method is effective in
alleviating the mode collapse problem in cGANs, and can control the diversity
of output images w.r.t specific-level features.
