We introduce a new category of generative autoencoders called automodulators. These networks can faithfully reproduce individual real-world input images like regular autoencoders, but also generate a fused sample from an arbitrary combination of several such images, allowing instantaneous 'style-mixing' and other new applications. An automodulator decouples the data flow of decoder operations from statistical properties thereof and uses the latent vector to modulate the former by the latter, with a principled approach for mutual disentanglement of decoder layers. Prior work has explored similar decoder architecture with GANs, but their focus has been on random sampling. A corresponding autoencoder could operate on real input images. For the first time, we show how to train such a general-purpose model with sharp outputs in high resolution, using novel training techniques, demonstrated on four image data sets. Besides style-mixing, we show state-of-the-art results in autoencoder comparison, and visual image quality nearly indistinguishable from state-of-the-art GANs. We expect the automodulator variants to become a useful building block for image applications and other data domains.

翻译：我们引入了一个新的基因自动解调器类别。 这些网络可以忠实复制像普通自动解调器那样的个体真实世界输入图像, 但也可以从一些任意组合的这类图像中生成一个引信样本, 允许瞬时“ 样式混合” 和其他新应用程序。 一个自动调制器解析器将来自其统计属性的解调器操作的数据流分离出来, 并使用潜在矢量来调节前者, 并采用一个原则性的方法对解调解调层进行相互分离。 先前的工作已经与 GANs 探索过类似的解调器结构, 但其重点是随机取样。 一个相应的自动解析器可以在真实输入图像上操作。 我们第一次展示了如何使用新式培训技术来训练这种通用模型, 在四个图像数据集中演示了高分辨率的高级输出。 除了样式混合外, 我们展示了在自动解析器比较和视觉图像质量方面最先进的结果, 几乎无法区分于状态的 GANs 。 我们期望自动解调器变式模型将变成一个有用的数据模型, 以及其它块域域 。