Hierarchical Modes Exploring in Generative Adversarial Networks

• URL: http://arxiv.org/abs/2003.08752v1
• Date: Thu, 5 Mar 2020 10:43:50 GMT
• Title: Hierarchical Modes Exploring in Generative Adversarial Networks
• Authors: Mengxiao Hu, Jinlong Li, Maolin Hu, Tao Hu
• Abstract summary: In conditional Generative Adversarial Networks (cGANs), when two different initial noises are paired with the same conditional information, minor modes are likely to collapse into large modes. We propose a hierarchical mode exploring method to alleviate mode collapse in cGANs by introducing a diversity measurement into the objective function as the regularization term.
• Score: 14.557204104822215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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