{"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/fader-networks-manipulating-images-by-sliding","title":"Fader Networks: Manipulating Images by Sliding Attributes","arxiv_id":"1706.00409","date":"2017-06-01","proceeding":null,"authors":["Guillaume Lample","Neil Zeghidour","Nicolas Usunier","Antoine Bordes","Ludovic Denoyer","Marc'Aurelio Ranzato"],"abstract":"This paper introduces a new encoder-decoder architecture that is trained to\nreconstruct images by disentangling the salient information of the image and\nthe values of attributes directly in the latent space. As a result, after\ntraining, our model can generate different realistic versions of an input image\nby varying the attribute values. By using continuous attribute values, we can\nchoose how much a specific attribute is perceivable in the generated image.\nThis property could allow for applications where users can modify an image\nusing sliding knobs, like faders on a mixing console, to change the facial\nexpression of a portrait, or to update the color of some objects. Compared to\nthe state-of-the-art which mostly relies on training adversarial networks in\npixel space by altering attribute values at train time, our approach results in\nmuch simpler training schemes and nicely scales to multiple attributes. We\npresent evidence that our model can significantly change the perceived value of\nthe attributes while preserving the naturalness of images.","url_abs":"http://arxiv.org/abs/1706.00409v2","url_pdf":"http://arxiv.org/pdf/1706.00409v2.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":"fader-networks-manipulating-images-by-sliding","repo_url":"https://github.com/facebookresearch/FaderNetworks","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"fader-networks-manipulating-images-by-sliding","repo_url":"https://github.com/sidwa/ae_thesis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"decoder","task_name":"Decoder"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1706.00409","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}