{"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/learn-to-synthesize-and-synthesize-to-learn","title":"Learn to synthesize and synthesize to learn","arxiv_id":"1905.00286","date":"2019-05-01","proceeding":null,"authors":["Behzad Bozorgtabar","Mohammad Saeed Rad","Hazim Kemal Ekenel","Jean-Philippe Thiran"],"abstract":"Attribute guided face image synthesis aims to manipulate attributes on a face\nimage. Most existing methods for image-to-image translation can either perform\na fixed translation between any two image domains using a single attribute or\nrequire training data with the attributes of interest for each subject.\nTherefore, these methods could only train one specific model for each pair of\nimage domains, which limits their ability in dealing with more than two\ndomains. Another disadvantage of these methods is that they often suffer from\nthe common problem of mode collapse that degrades the quality of the generated\nimages. To overcome these shortcomings, we propose attribute guided face image\ngeneration method using a single model, which is capable to synthesize multiple\nphoto-realistic face images conditioned on the attributes of interest. In\naddition, we adopt the proposed model to increase the realism of the simulated\nface images while preserving the face characteristics. Compared to existing\nmodels, synthetic face images generated by our method present a good\nphotorealistic quality on several face datasets. Finally, we demonstrate that\ngenerated facial images can be used for synthetic data augmentation, and\nimprove the performance of the classifier used for facial expression\nrecognition.","url_abs":"http://arxiv.org/abs/1905.00286v1","url_pdf":"http://arxiv.org/pdf/1905.00286v1.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":"learn-to-synthesize-and-synthesize-to-learn","repo_url":"https://github.com/CreativePapers/Learn-to-Synthesize-and-Synthesize-to-Learn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"facial-expression-recognition-1","task_name":"Facial Expression Recognition"},{"task_slug":"facial-expression-recognition","task_name":"Facial Expression Recognition (FER)"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"image-to-image-translation","task_name":"Image-to-Image Translation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}