论文标题
改善具有自回归流量的顺序潜在变量模型
Improving Sequential Latent Variable Models with Autoregressive Flows
论文作者
论文摘要
我们提出了一种基于自回归归一化流量的序列建模的方法。每个自回归变换跨时间起作用，都是一个运动的参考框架，消除时间相关性并简化了高级动力学的建模。该技术提供了一种简单的通用方法，可通过与现有和经典技术的联系来改进序列建模。我们通过基于独立流量的模型和顺序潜在变量模型中的组件演示了所提出的方法。结果显示在三个基准视频数据集中，基于回归流的动力学改善了基线模型上的对数可能性性能。最后，我们说明了使用基于流动的动力学的去相关和改进的泛化属性。
We propose an approach for improving sequence modeling based on autoregressive normalizing flows. Each autoregressive transform, acting across time, serves as a moving frame of reference, removing temporal correlations, and simplifying the modeling of higher-level dynamics. This technique provides a simple, general-purpose method for improving sequence modeling, with connections to existing and classical techniques. We demonstrate the proposed approach both with standalone flow-based models and as a component within sequential latent variable models. Results are presented on three benchmark video datasets, where autoregressive flow-based dynamics improve log-likelihood performance over baseline models. Finally, we illustrate the decorrelation and improved generalization properties of using flow-based dynamics.
