论文标题
协助对手改善GAN训练
Assisting the Adversary to Improve GAN Training
论文作者
论文摘要
提高gan的稳定性和性能的一些最流行的方法涉及约束或正规化歧视者。在本文中，我们考虑了一种在很大程度上被忽略的正规化技术，我们称之为对手的助手（Advas）。我们使用与先前工作的观点不同的观点来激励这一点。具体而言，我们考虑理论分析与实践之间的常见不匹配：分析通常假设歧视者在每次迭代中都达到了其最佳状态。在实践中，这本质上是永远不正确的，通常会导致发电机的梯度估计差。为了解决这个问题，Advas是基于训练歧视者的梯度的规范对发电机施加的一种理论上动机的惩罚。这鼓励发电机朝歧视器最佳的点发展。我们演示了将ADVA应用于几个GAN目标，数据集和网络体系结构的效果。结果表明，理论与实践之间的不匹配减少，并且ADVA可以通过FID得分来改善GAN训练的改善。
Some of the most popular methods for improving the stability and performance of GANs involve constraining or regularizing the discriminator. In this paper we consider a largely overlooked regularization technique which we refer to as the Adversary's Assistant (AdvAs). We motivate this using a different perspective to that of prior work. Specifically, we consider a common mismatch between theoretical analysis and practice: analysis often assumes that the discriminator reaches its optimum on each iteration. In practice, this is essentially never true, often leading to poor gradient estimates for the generator. To address this, AdvAs is a theoretically motivated penalty imposed on the generator based on the norm of the gradients used to train the discriminator. This encourages the generator to move towards points where the discriminator is optimal. We demonstrate the effect of applying AdvAs to several GAN objectives, datasets and network architectures. The results indicate a reduction in the mismatch between theory and practice and that AdvAs can lead to improvement of GAN training, as measured by FID scores.
