Abstract
An interpretable comparison of generative models requires the identification of sample types produced more frequently by each of the involved models. While several quantitative scores have been proposed in the literature to rank different generative models, such score-based evaluations do not reveal the nuanced differences between the generative models in capturing various sample types. In this work, we propose a method called Fourier-based Identification of Novel Clusters (FINC) to identify modes produced by a generative model with a higher frequency in comparison to a reference distribution. FINC provides a scalable stochastic algorithm based on random Fourier features to estimate the eigenspace of kernel covariance matrices of two generative models and utilize the principal eigendirections to detect the sample types present more dominantly in each model. We demonstrate the application of the FINC method to standard computer vision datasets and generative model frameworks. Our numerical results suggest the scalability and efficiency of the developed Fourier-based method in highlighting the sample types captured with different frequencies by widely-used generative models.
Abstract (translated)
一种可解释的比较生成模型需要确定每个涉及模型的产生样本类型的更多元。虽然文献中已经提出了几个定量的评分来比较不同的生成模型,但这些评分基于分数的评估并没有揭示生成模型在捕捉各种样本类型方面的细微差异。在本文中,我们提出了一种方法叫做基于傅里叶特征的新兴聚类识别方法(FINC)来识别比参考分布产生更高频率的生成模型的模态。FINC基于随机傅里叶特征提供了一个可扩展的随机算法,用于估计两个生成模型的核协方差矩阵的 eigen space,并利用主 eigendirections 检测每个模型中出现更主要样本类型的样本类型。我们证明了FINC方法在标准计算机视觉数据集和生成模型框架中的应用。我们的数值结果表明,基于傅里叶特征的方法在强调使用广泛使用的生成模型捕捉不同频率样本类型方面具有可扩展性和效率。
URL
https://arxiv.org/abs/2405.02700
