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.

本文介绍了区分度对比散度方法，该方法基于 WGANs 的判别器性质和 WGANs 与能量基模型之间的关系，并利用 WGANs 判别器所包含的信息来提高图像生成的质量。我们使用随机梯度的方式实现判别器对样本密度比率的估计，然后在 Langevin 动力学的多个时间步骤中生成样本。在合成数据和实际图像生成基准测试方面，我们证明了这种改进可以显著提高图像生成。

判别器差分发散: 探索鉴别器能量的半自摆脱生成建模