Title: Hybrid Generative Models for Two-Dimensional Datasets Show Chinese title

Title: 二维数据集的混合生成模型

Abstract: Two-dimensional array-based datasets are pervasive in a variety of domains. Current approaches for generative modeling have typically been limited to conventional image datasets and performed in the pixel domain which do not explicitly capture the correlation between pixels. Additionally, these approaches do not extend to scientific and other applications where each element value is continuous and is not limited to a fixed range. In this paper, we propose a novel approach for generating two-dimensional datasets by moving the computations to the space of representation bases and show its usefulness for two different datasets, one from imaging and another from scientific computing. The proposed approach is general and can be applied to any dataset, representation basis, or generative model. We provide a comprehensive performance comparison of various combinations of generative models and representation basis spaces. We also propose a new evaluation metric which captures the deficiency of generating images in pixel space. Show Chinese abstract

Abstract: 二维数组数据集在各种领域中普遍存在。 当前的生成建模方法通常仅限于传统的图像数据集，并在像素域中进行，这并未明确捕捉像素之间的相关性。 此外，这些方法无法扩展到科学和其他应用，其中每个元素值是连续的，并不限于固定范围。 在本文中，我们提出了一种新的生成二维数据集的方法，通过将计算转移到表示基空间，并展示了其在两个不同数据集中的有用性，一个来自成像，另一个来自科学计算。 所提出的方法是通用的，可以应用于任何数据集、表示基或生成模型。 我们提供了各种生成模型和表示基空间组合的全面性能比较。 我们还提出了一种新的评估指标，用于捕捉在像素空间中生成图像的不足。