As image generative models continue to increase not only in their fidelity but also in their ubiquity the development of tools that leverage direct interaction with their internal mechanisms in an interpretable way has received little attention In this work we introduce a system that allows users to develop a better understanding of the model through interaction and experimentation By giving users the ability to replace activation functions of a generative network with parametric ones and a way to set the parameters of these functions we introduce an alternative approach to control the networks output We demonstrate the use of our method on StyleGAN2 and BigGAN networks trained on FFHQ and ImageNet respectively.

翻译：随着图像生成模型不仅在保真度上持续提升，其应用也日益普及，然而，利用可解释方式直接与其内部机制交互的工具开发却鲜受关注。本研究提出一个系统，使用户能够通过交互与实验更好地理解模型。通过赋予用户将生成网络的激活函数替换为参数化函数的能力，并提供设置这些函数参数的方法，我们引入了一种控制网络输出的替代途径。我们在分别基于FFHQ和ImageNet训练的StyleGAN2和BigGAN网络上演示了该方法的应用。