{"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/it-takes-only-two-adversarial-generator","title":"It Takes (Only) Two: Adversarial Generator-Encoder Networks","arxiv_id":"1704.02304","date":"2017-04-07","proceeding":null,"authors":["Dmitry Ulyanov","Andrea Vedaldi","Victor Lempitsky"],"abstract":"We present a new autoencoder-type architecture that is trainable in an\nunsupervised mode, sustains both generation and inference, and has the quality\nof conditional and unconditional samples boosted by adversarial learning.\nUnlike previous hybrids of autoencoders and adversarial networks, the\nadversarial game in our approach is set up directly between the encoder and the\ngenerator, and no external mappings are trained in the process of learning. The\ngame objective compares the divergences of each of the real and the generated\ndata distributions with the prior distribution in the latent space. We show\nthat direct generator-vs-encoder game leads to a tight coupling of the two\ncomponents, resulting in samples and reconstructions of a comparable quality to\nsome recently-proposed more complex architectures.","url_abs":"http://arxiv.org/abs/1704.02304v3","url_pdf":"http://arxiv.org/pdf/1704.02304v3.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":"it-takes-only-two-adversarial-generator","repo_url":"https://github.com/DmitryUlyanov/AGE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"two","task_name":"Vocal Bursts Valence Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1704.02304","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}