论文标题
具有矩阵指数的生成流量
Generative Flows with Matrix Exponential
论文作者
论文摘要
生成流量模型享有由可逆转可能性和有效采样的特性，这些样本由一系列可逆函数组成。在本文中，我们将矩阵指数纳入生成流中。矩阵指数是从矩阵到可逆矩阵的地图，此属性适用于生成流。基于矩阵指数，我们提出了矩阵指数耦合层，这些耦合层是仿射耦合层和矩阵指数可逆的1 x 1卷积的一般情况，在训练过程中不会倒塌。我们修改了网络体系结构，以使训练史上并有意义地加快培训过程。我们的实验表明，我们的模型在生成流模型之间的密度估计上取得了出色的性能。
Generative flows models enjoy the properties of tractable exact likelihood and efficient sampling, which are composed of a sequence of invertible functions. In this paper, we incorporate matrix exponential into generative flows. Matrix exponential is a map from matrices to invertible matrices, this property is suitable for generative flows. Based on matrix exponential, we propose matrix exponential coupling layers that are a general case of affine coupling layers and matrix exponential invertible 1 x 1 convolutions that do not collapse during training. And we modify the networks architecture to make trainingstable andsignificantly speed up the training process. Our experiments show that our model achieves great performance on density estimation amongst generative flows models.
