Evaluation of generative models is mostly based on the comparison between the estimated distribution and the ground truth distribution in a certain feature space. To embed samples into informative features, previous works often use convolutional neural networks optimized for classification, which is criticized by recent studies. Therefore, various feature spaces have been explored to discover alternatives. Among them, a surprising approach is to use a randomly initialized neural network for feature embedding. However, the fundamental basis to employ the random features has not been sufficiently justified. In this paper, we rigorously investigate the feature space of models with random weights in comparison to that of trained models. Furthermore, we provide an empirical evidence to choose networks for random features to obtain consistent and reliable results. Our results indicate that the features from random networks can evaluate generative models well similarly to those from trained networks, and furthermore, the two types of features can be used together in a complementary way.
翻译:对基因模型的评估主要基于对某一特征空间的估计分布和地面真实分布的比较。为了将样本嵌入信息特征中,以往的作品经常使用最优化的进化神经网络进行分类,最近的研究批评了这种分类。因此,探索了各种特征空间以发现替代物。其中一种令人惊讶的方法是使用随机初始神经网络进行特征嵌入。然而,使用随机特征的基本依据不够充分。在本文件中,我们严格调查与经过培训的模型相比,随机重量模型的特征空间。此外,我们提供了经验证据,为随机特征选择网络,以获得一致和可靠的结果。我们的结果表明,随机网络的特征可以与受过培训的网络的特征非常相似地评估基因模型,此外,两种特征可以相辅相成地一起使用。