论文标题
非线性等距流形学习，用于注入归一流的流量
Nonlinear Isometric Manifold Learning for Injective Normalizing Flows
论文作者
论文摘要
为了使用归一化流量对歧管数据进行模拟数据，我们使用等距自动编码器来设计具有显式倒置的嵌入，而不会扭曲概率分布。使用异构体可将多种学习和密度估计分开，并使这两个部分的训练都可以高精度。因此，与现有的注射式归一化流相比，模型选择和调整被简化。应用于（大约）平面流形的数据集，合并的方法生成了高质量的数据。
To model manifold data using normalizing flows, we employ isometric autoencoders to design embeddings with explicit inverses that do not distort the probability distribution. Using isometries separates manifold learning and density estimation and enables training of both parts to high accuracy. Thus, model selection and tuning are simplified compared to existing injective normalizing flows. Applied to data sets on (approximately) flat manifolds, the combined approach generates high-quality data.
