{"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/automated-deep-photo-style-transfer","title":"Automated Deep Photo Style Transfer","arxiv_id":"1901.03915","date":"2019-01-12","proceeding":null,"authors":["Sebastian Penhouët","Paul Sanzenbacher"],"abstract":"Photorealism is a complex concept that cannot easily be formulated\nmathematically. Deep Photo Style Transfer is an attempt to transfer the style\nof a reference image to a content image while preserving its photorealism. This\nis achieved by introducing a constraint that prevents distortions in the\ncontent image and by applying the style transfer independently for semantically\ndifferent parts of the images. In addition, an automated segmentation process\nis presented that consists of a neural network based segmentation method\nfollowed by a semantic grouping step. To further improve the results a measure\nfor image aesthetics is used and elaborated. If the content and the style image\nare sufficiently similar, the result images look very realistic. With the\nautomation of the image segmentation the pipeline becomes completely\nindependent from any user interaction, which allows for new applications.","url_abs":"http://arxiv.org/abs/1901.03915v1","url_pdf":"http://arxiv.org/pdf/1901.03915v1.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":"automated-deep-photo-style-transfer","repo_url":"https://github.com/Spenhouet/automated-deep-photo-style-transfer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"style-transfer","task_name":"Style Transfer"}],"methods":[{"method_slug":"auxiliary-classifier","method_name":"Auxiliary Classifier"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dilated-convolution","method_name":"Dilated Convolution"},{"method_slug":"nima","method_name":"NIMA"},{"method_slug":"pspnet","method_name":"PSPNet"},{"method_slug":"pyramid-pooling-module","method_name":"Pyramid Pooling Module"},{"method_slug":"relu","method_name":"ReLU"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1901.03915","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}