{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pioneer-networks-progressively-growing","title":"Pioneer Networks: Progressively Growing Generative Autoencoder","arxiv_id":"1807.03026","date":"2018-07-09","proceeding":null,"authors":["Ari Heljakka","Arno Solin","Juho Kannala"],"abstract":"We introduce a novel generative autoencoder network model that learns to\nencode and reconstruct images with high quality and resolution, and supports\nsmooth random sampling from the latent space of the encoder. Generative\nadversarial networks (GANs) are known for their ability to simulate random\nhigh-quality images, but they cannot reconstruct existing images. Previous\nworks have attempted to extend GANs to support such inference but, so far, have\nnot delivered satisfactory high-quality results. Instead, we propose the\nProgressively Growing Generative Autoencoder (PIONEER) network which achieves\nhigh-quality reconstruction with $128{\\times}128$ images without requiring a\nGAN discriminator. We merge recent techniques for progressively building up the\nparts of the network with the recently introduced adversarial encoder-generator\nnetwork. The ability to reconstruct input images is crucial in many real-world\napplications, and allows for precise intelligent manipulation of existing\nimages. We show promising results in image synthesis and inference, with\nstate-of-the-art results in CelebA inference tasks.","url_abs":"http://arxiv.org/abs/1807.03026v2","url_pdf":"http://arxiv.org/pdf/1807.03026v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"pioneer-networks-progressively-growing","repo_url":"https://github.com/AaltoVision/pioneer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.03026","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.03026"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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