  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.
