论文标题
生成对抗网络中探索的分层模式
Hierarchical Modes Exploring in Generative Adversarial Networks
论文作者
论文摘要
在条件生成的对抗网络（CGAN）中，当两个不同的初始噪声与相同的条件信息串联时，它们的输出之间的距离相对较小，这使得次要模式可能崩溃成大型模式。为了防止这种情况发生，我们提出了一种分层模式探索方法，以通过将多样性测量作为正则化项引入目标函数，以减轻CGAN的模式崩溃。我们还将预期扩展（ERE）的预期比率引入了正规化项中，通过最大程度地减少距离和ERE的实际变化之间的差异之和，我们可以控制生成的图像W.R.T特定级特征的多样性。我们验证了针对四个条件图像综合任务的提议算法，包括分类生成，配对和未配对的图像翻译以及文本对图像生成。定性和定量结果都表明，所提出的方法有效地减轻了CGAN中的模式崩溃问题，并且可以控制输出图像的多样性W.R.T特定级别的特征。
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.
