论文标题
拓扑距离：一种基于拓扑的方法，用于评估生成对抗网络
Topology Distance: A Topology-Based Approach For Evaluating Generative Adversarial Networks
论文作者
论文摘要
自动评估生成对抗网络（GAN）的优点一直是机器学习领域的挑战。在这项工作中，我们提出了与现有措施的补充距离：拓扑距离（TD），背后的主要思想是将真实数据的潜在歧管与生成数据的潜在多种形态进行比较。更具体地说，我们在图像特征上构建了越野河 - 钢制复合物，并根据两个流形的持久 - 同学组的差异来定义TD。我们将TD与现场最常用和最相关的度量进行了比较，包括在各种数据集中的一系列实验中，包括IS分数（IS），Frechet Inception距离（FID），内核成立距离（KID）和几何得分（GS）。我们证明了我们提出的方法比上述指标具有独特的优势和优势。我们提出的有利于TD的理论结果和理论论点的结合强烈支持这样的说法，即TD是一个有力的候选指标，研究人员在旨在自动评估GANS学习的良好时可以使用它。
Automatic evaluation of the goodness of Generative Adversarial Networks (GANs) has been a challenge for the field of machine learning. In this work, we propose a distance complementary to existing measures: Topology Distance (TD), the main idea behind which is to compare the geometric and topological features of the latent manifold of real data with those of generated data. More specifically, we build Vietoris-Rips complex on image features, and define TD based on the differences in persistent-homology groups of the two manifolds. We compare TD with the most commonly used and relevant measures in the field, including Inception Score (IS), Frechet Inception Distance (FID), Kernel Inception Distance (KID) and Geometry Score (GS), in a range of experiments on various datasets. We demonstrate the unique advantage and superiority of our proposed approach over the aforementioned metrics. A combination of our empirical results and the theoretical argument we propose in favour of TD, strongly supports the claim that TD is a powerful candidate metric that researchers can employ when aiming to automatically evaluate the goodness of GANs' learning.
