论文标题
视频生成的潜在神经微分方程
Latent Neural Differential Equations for Video Generation
论文作者
论文摘要
生成的对手网络最近显示了视频生成的希望，这是建立图像生成成功的基础，同时还解决了新的挑战：时间。尽管在一些早期工作中分析了时间，但文献尚未随着时间建模的发展而充分发展。我们研究神经微分方程对视频发电的时间动力学建模的影响。神经微分方程的范式提出了许多理论上的优势，包括视频发电中时间的第一个连续代表。为了解决神经微分方程的影响，我们研究了时间模型的变化如何影响产生的视频质量。我们的结果为神经微分方程的使用提供了支持，作为对较老的颞发电机的简单替代。在保持运行时间相似并减少参数计数的同时，我们以64 $ \ times $ 64 Pixel无条件视频生成生产新的最新模型，其成立分数为15.20。
Generative Adversarial Networks have recently shown promise for video generation, building off of the success of image generation while also addressing a new challenge: time. Although time was analyzed in some early work, the literature has not adequately grown with temporal modeling developments. We study the effects of Neural Differential Equations to model the temporal dynamics of video generation. The paradigm of Neural Differential Equations presents many theoretical strengths including the first continuous representation of time within video generation. In order to address the effects of Neural Differential Equations, we investigate how changes in temporal models affect generated video quality. Our results give support to the usage of Neural Differential Equations as a simple replacement for older temporal generators. While keeping run times similar and decreasing parameter count, we produce a new state-of-the-art model in 64$\times$64 pixel unconditional video generation, with an Inception Score of 15.20.
