{"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/adversarial-information-factorization","title":"Adversarial Information Factorization","arxiv_id":"1711.05175","date":"2017-11-14","proceeding":"ICLR 2019 5","authors":["Antonia Creswell","Yumnah Mohamied","Biswa Sengupta","Anil A. Bharath"],"abstract":"We propose a novel generative model architecture designed to learn\nrepresentations for images that factor out a single attribute from the rest of\nthe representation. A single object may have many attributes which when altered\ndo not change the identity of the object itself. Consider the human face; the\nidentity of a particular person is independent of whether or not they happen to\nbe wearing glasses. The attribute of wearing glasses can be changed without\nchanging the identity of the person. However, the ability to manipulate and\nalter image attributes without altering the object identity is not a trivial\ntask. Here, we are interested in learning a representation of the image that\nseparates the identity of an object (such as a human face) from an attribute\n(such as 'wearing glasses'). We demonstrate the success of our factorization\napproach by using the learned representation to synthesize the same face with\nand without a chosen attribute. We refer to this specific synthesis process as\nimage attribute manipulation. We further demonstrate that our model achieves\ncompetitive scores, with state of the art, on a facial attribute classification\ntask.","url_abs":"http://arxiv.org/abs/1711.05175v2","url_pdf":"http://arxiv.org/pdf/1711.05175v2.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":"adversarial-information-factorization","repo_url":"https://github.com/ToniCreswell/attribute-cVAEGAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"facial-attribute-classification","task_name":"Facial Attribute Classification"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"object","task_name":"Object"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.05175","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}