The conventional understanding of adversarial training in generative adversarial networks (GANs) is that the discriminator is trained to estimate a divergence, and the generator learns to minimize this divergence. We argue that despite the fact that many variants of GANs were developed following this paradigm, the current theoretical understanding of GANs and their practical algorithms are inconsistent. In this paper, we leverage Wasserstein gradient flows which characterize the evolution of particles in the sample space, to gain theoretical insights and algorithmic inspiration of GANs. We introduce a unified generative modeling framework - MonoFlow: the particle evolution is rescaled via a monotonically increasing mapping of the log density ratio. Under our framework, adversarial training can be viewed as a procedure first obtaining MonoFlow's vector field via training the discriminator and the generator learns to draw the particle flow defined by the corresponding vector field. We also reveal the fundamental difference between variational divergence minimization and adversarial training. This analysis helps us to identify what types of generator loss functions can lead to the successful training of GANs and suggest that GANs may have more loss designs beyond the literature (e.g., non-saturated loss), as long as they realize MonoFlow. Consistent empirical studies are included to validate the effectiveness of our framework.

翻译：生成对抗网络（GANs）中对抗训练的传统理解是：判别器被训练用于估计某种散度，而生成器则学习最小化该散度。我们指出，尽管许多GAN变体遵循这一范式开发，但目前GAN的理论理解与其实际算法之间存在不一致性。本文利用刻画样本空间中粒子演化的Wasserstein梯度流，为GAN的理论洞见和算法启发提供了新视角。我们引入统一的生成建模框架——MonoFlow：粒子演化通过对数密度比的单调递增映射进行重新缩放。在该框架下，对抗训练可被视为一个过程：首先通过训练判别器获取MonoFlow的向量场，随后生成器学习绘制由该向量场定义的粒子流。我们还揭示了变分散度最小化与对抗训练之间的根本差异。这一分析有助于识别何种生成器损失函数能促成GAN的成功训练，并表明只要实现了MonoFlow，GAN可能拥有超越现有文献（如非饱和损失）的更多损失设计。我们包含了一致的实验研究，以验证该框架的有效性。