论文标题
歧视差异差异：通过探索歧视器的能量来进行半损坏的生成建模
Discriminator Contrastive Divergence: Semi-Amortized Generative Modeling by Exploring Energy of the Discriminator
论文作者
论文摘要
生成对抗网络（GAN）在建模高维数据方面表现出了巨大的希望。 GAN的学习目标通常可以最大程度地减少某种度量的差异，\ textit {e.g。}，$ f $ -Divergence〜（$ f $ -gans）或积分概率度量〜（Wasserstein Gans）。以$ f $ divergence为目标函数，鉴别器基本上估计了密度比，并且估计比率可用于进一步提高发电机的样本质量。但是，如何利用Wasserstein Gans（Wgan）歧视者中包含的信息的探索较少。在本文中，我们介绍了歧视者的对比差异，这是由Wgan歧视者的特性以及WGAN与基于能量的模型之间的关系充分激励的。与标准gan相比，在直接利用发电机来获取新样品的情况下，我们的方法提出了一个半损坏的生成过程，其中样品是用发电机的输出作为初始状态的。然后使用鉴别器的梯度进行了几个步骤。我们证明了对合成数据和几个现实世界图像生成基准的显着改善产生的好处。
Generative Adversarial Networks (GANs) have shown great promise in modeling high dimensional data. The learning objective of GANs usually minimizes some measure discrepancy, \textit{e.g.}, $f$-divergence~($f$-GANs) or Integral Probability Metric~(Wasserstein GANs). With $f$-divergence as the objective function, the discriminator essentially estimates the density ratio, and the estimated ratio proves useful in further improving the sample quality of the generator. However, how to leverage the information contained in the discriminator of Wasserstein GANs (WGAN) is less explored. In this paper, we introduce the Discriminator Contrastive Divergence, which is well motivated by the property of WGAN's discriminator and the relationship between WGAN and energy-based model. Compared to standard GANs, where the generator is directly utilized to obtain new samples, our method proposes a semi-amortized generation procedure where the samples are produced with the generator's output as an initial state. Then several steps of Langevin dynamics are conducted using the gradient of the discriminator. We demonstrate the benefits of significant improved generation on both synthetic data and several real-world image generation benchmarks.
