This work presents an analysis of the discriminators used in Generative Adversarial Networks (GANs) for Video. We show that unconstrained video discriminator architectures induce a loss surface with high curvature which make optimization difficult. We also show that this curvature becomes more extreme as the maximal kernel dimension of video discriminators increases. With these observations in hand, we propose a methodology for the design of a family of efficient Lower-Dimensional Video Discriminators for GANs (LDVD-GANs). The proposed methodology improves the performance and efficiency of video GAN models it is applied to and demonstrates good performance on complex and diverse datasets such as UCF-101. In particular, we show that LDVDs can double the performance of Temporal-GANs and provide for state-of-the-art performance on a single GPU using the proposed methodology.

这项工作介绍了视频生成对抗网络（GAN）中使用的鉴别器的分析。我们表明，不受约束的视频鉴别器架构会导致具有高曲率的损耗表面，从而使优化变得困难。我们还表明，随着视频鉴别器的最大核尺寸增加，该曲率变得更加极端。有了这些观察，我们提出了一种用于设计GAN的高效低维视频鉴别器（LDVD-GAN）系列的方法。所提出的方法提高了视频GAN模型应用于视频GAN模型的性能和效率，并在诸如UCF-101等复杂多样的数据集上展示了良好的性能。尤其是，