论文标题
成对歧视者对对抗训练的好处
The Benefits of Pairwise Discriminators for Adversarial Training
论文作者
论文摘要
对抗训练方法通常通过求解两人游戏来对齐分布。但是，在大多数当前的配方中，即使发电机与数据完美对齐，次优歧视器仍然可以使两者分开。没有其他正则化，不稳定可以表现为无休止的游戏。在本文中，我们通过利用成对歧视器来介绍一个目标，并表明只有发电机需要收敛。如果实现的话，将与任何歧视者保持一致。我们为局部收敛提供了足够的条件；表征应指导歧视者和发电机选择的能力平衡；并构建最小歧视者的示例。从经验上讲，我们说明了方法对合成示例的理论和有效性。此外，我们表明，从我们的方法中得出的实用方法可以更好地生成更高分辨率的图像。
Adversarial training methods typically align distributions by solving two-player games. However, in most current formulations, even if the generator aligns perfectly with data, a sub-optimal discriminator can still drive the two apart. Absent additional regularization, the instability can manifest itself as a never-ending game. In this paper, we introduce a family of objectives by leveraging pairwise discriminators, and show that only the generator needs to converge. The alignment, if achieved, would be preserved with any discriminator. We provide sufficient conditions for local convergence; characterize the capacity balance that should guide the discriminator and generator choices; and construct examples of minimally sufficient discriminators. Empirically, we illustrate the theory and the effectiveness of our approach on synthetic examples. Moreover, we show that practical methods derived from our approach can better generate higher-resolution images.
