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We introduce a novel generative autoencoder network model that learns toencode and reconstruct images with high quality and resolution, and supportssmooth random sampling from the latent space of the encoder. Generativeadversarial networks (GANs) are known for their ability to simulate randomhigh-quality images, but they cannot reconstruct existing images. Previousworks have attempted to extend GANs to support such inference but, so far, havenot delivered satisfactory high-quality results. Instead, we propose theProgressively Growing Generative Autoencoder (PIONEER) network which achieveshigh-quality reconstruction with $128{\times}128$ images without requiring aGAN discriminator. We merge recent techniques for progressively building up theparts of the network with the recently introduced adversarial encoder-generatornetwork. The ability to reconstruct input images is crucial in many real-worldapplications, and allows for precise intelligent manipulation of existingimages. We show promising results in image synthesis and inference, withstate-of-the-art results in CelebA inference tasks.

Pioneer Networks: Progressively Growing Generative Autoencoder