用可调激活函数控制生成图像风格，让模型更易懂、更好用。
Controlling the image generation process with parametric activation functions
- 用可参数化的激活函数替换生成网络中的原有函数
- 在StyleGAN2和BigGAN上实现对生成结果的可控调节
- 适合想深入理解或定制生成过程的研究者
随着图像生成模型在质量与普及度上的不断提升，直接交互并以可解释方式操控其内部机制的工具却鲜有发展。本文提出一种系统，使用户可通过替换生成网络中的激活函数为可参数化形式，并调节其参数来干预生成过程，从而加深对模型的理解。我们在训练于FFHQ数据集的StyleGAN2和训练于ImageNet数据集的BigGAN上验证了该方法的有效性，展示了通过调整激活函数参数，可实现对生成图像风格的精准控制。
原文摘要 · Abstract (English)
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.
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