论文标题
Softflow：用于歧管流动归一流的概率框架
SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds
论文作者
论文摘要
基于流的生成模型由相同维度的两个随机变量之间的可逆变换组成。因此，如果数据分布的尺寸与基础目标分布的尺寸不符，则基于流的模型无法得到充分的培训。在本文中，我们提出了Softflow，这是一种概率的框架，用于训练在多种流动上的流动。为了避开尺寸不匹配问题，软流量估计了扰动输入数据的条件分布，而不是直接学习数据分布。我们在实验上表明，软丝可以捕获流形数据的先天结构，并生成与常规基于流的模型不同的高质量样本。此外，我们将提出的框架应用于3D点云，以减轻为基于流的模型形成薄结构的困难。提出的3D点云模型，即软点流，可以更准确地估算各种形状的分布，并在点云生成中实现最先进的性能。
Flow-based generative models are composed of invertible transformations between two random variables of the same dimension. Therefore, flow-based models cannot be adequately trained if the dimension of the data distribution does not match that of the underlying target distribution. In this paper, we propose SoftFlow, a probabilistic framework for training normalizing flows on manifolds. To sidestep the dimension mismatch problem, SoftFlow estimates a conditional distribution of the perturbed input data instead of learning the data distribution directly. We experimentally show that SoftFlow can capture the innate structure of the manifold data and generate high-quality samples unlike the conventional flow-based models. Furthermore, we apply the proposed framework to 3D point clouds to alleviate the difficulty of forming thin structures for flow-based models. The proposed model for 3D point clouds, namely SoftPointFlow, can estimate the distribution of various shapes more accurately and achieves state-of-the-art performance in point cloud generation.
