  Autoencoder networks are unsupervised approaches aiming at combining
generative and representational properties by learning simultaneously an
encoder-generator map. Although studied extensively, the issues of whether they
have the same generative power of GANs, or learn disentangled representations,
have not been fully addressed. We introduce an autoencoder that tackles these
issues jointly, which we call Adversarial Latent Autoencoder (ALAE). It is a
general architecture that can leverage recent improvements on GAN training
procedures. We designed two autoencoders: one based on a MLP encoder, and
another based on a StyleGAN generator, which we call StyleALAE. We verify the
disentanglement properties of both architectures. We show that StyleALAE can
not only generate 1024x1024 face images with comparable quality of StyleGAN,
but at the same resolution can also produce face reconstructions and
manipulations based on real images. This makes ALAE the first autoencoder able
to compare with, and go beyond the capabilities of a generator-only type of
architecture.
