Pioneer Networks: Progressively Growing Generative Autoencoder
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
- GenMind Ltd.
We introduce a novel generative autoencoder network model that learns to encode and reconstruct images with high quality and resolution, and supports smooth random sampling from the latent space of the encoder. Generative adversarial networks (GANs) are known for their ability to simulate random high-quality images, but they cannot reconstruct existing images. Previous works have attempted to extend GANs to support such inference but, so far, have not delivered satisfactory high-quality results. Instead, we propose the Progressively Growing Generative Autoencoder (Pioneer) network which achieves high-quality reconstruction with images without requiring a GAN discriminator. We merge recent techniques for progressively building up the parts of the network with the recently introduced adversarial encoder–generator network. The ability to reconstruct input images is crucial in many real-world applications, and allows for precise intelligent manipulation of existing images. We show promising results in image synthesis and inference, with state-of-the-art results in CelebA inference tasks.
|Title of host publication||Computer Vision – ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers|
|Editors||Greg Mori, C.V. Jawahar, Konrad Schindler, Hongdong Li|
|Publication status||Published - 2019|
|MoE publication type||A4 Article in a conference publication|
|Event||Asian Conference on Computer Vision - Perth, Australia|
Duration: 2 Dec 2018 → 6 Dec 2018
Conference number: 14
|Name||Lecture notes in computer science|
|Conference||Asian Conference on Computer Vision|
|Period||02/12/2018 → 06/12/2018|
- Autoencoder, Computer vision, Generative models