We investigate how generated structures of GANs correlate with their activations in hidden layers, with the purpose of better understanding the inner workings of those models and being able to paint structures with unconditionally trained GANs. This gives us more control over the generated images, allowing to generate them from a semantic segmentation map while not requiring such a segmentation in the training data. To this end we introduce the concept of tileable features, allowing us to identify activations that work well for painting.

翻译：本研究探讨了生成对抗网络（GAN）的生成结构如何与其隐藏层中的激活相关联，旨在深入理解这些模型的内部工作机制，并能够利用无条件训练的GAN绘制结构。这使我们能够对生成图像实现更强的控制，从而能够基于语义分割图生成图像，而无需在训练数据中包含此类分割。为此，我们引入了可平铺特征的概念，使我们能够识别适用于绘制的激活模式。