潜在空间制图学：生成模型的广义度量启发测度和基于测度的变换
Latent Space Cartography: Generalised Metric-Inspired Measures and Measure-Based Transformations for Generative Models
摘要：
深度生成模型是通过映射到低维潜在空间来学习高维数据空间上的数据分布的通用工具。我们提供了一个研究潜在的空间几何形状和扩展和建立在以前的结果黎曼度量。我们展示了一类启发式措施如何在寻找有意义的，特定于问题的距离方面提供更大的灵活性，以及它如何应用于不同的生成器类型，例如语言和其他序列建模中常用的自回归生成器。我们进一步演示了如何使用之前在制图学中研究的扩散启发的变换来平滑潜在空间，并根据所选的度量来拉伸它们。除了直接在潜在空间中提供更有意义的距离外，这还为新型数据可视化提供了独特的工具。我们相信，所提出的方法可以成为一个有价值的工具，用于研究潜在空间的结构和生成模型的学习数据分布。
英文摘要：
Deep generative models are universal tools for learning data distributions on high dimensional data spaces via a mapping to lower dimensional latent spaces. We provide a study of latent space geometries and extend and build upon previous results on Riemannian metrics. We show how a class of heuristic measures gives more flexibility in finding meaningful, problem-specific distances, and how it can be applied to diverse generator types such as autoregressive generators commonly used in e.g. language and other sequence modeling. We further demonstrate how a diffusion-inspired transformation previously studied in cartography can be used to smooth out latent spaces, stretching them according to a chosen measure. In addition to providing more meaningful distances directly in latent space, this also provides a unique tool for novel kinds of data visualizations. We believe that the proposed methods can be a valuable tool for studying the structure of latent spaces and learned data distributions of generative models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
