{"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/neural-style-transfer-a-review","title":"Neural Style Transfer: A Review","arxiv_id":"1705.04058","date":"2017-05-11","proceeding":null,"authors":["Yongcheng Jing","Yezhou Yang","Zunlei Feng","Jingwen Ye","Yizhou Yu","Mingli Song"],"abstract":"The seminal work of Gatys et al. demonstrated the power of Convolutional\nNeural Networks (CNNs) in creating artistic imagery by separating and\nrecombining image content and style. This process of using CNNs to render a\ncontent image in different styles is referred to as Neural Style Transfer\n(NST). Since then, NST has become a trending topic both in academic literature\nand industrial applications. It is receiving increasing attention and a variety\nof approaches are proposed to either improve or extend the original NST\nalgorithm. In this paper, we aim to provide a comprehensive overview of the\ncurrent progress towards NST. We first propose a taxonomy of current algorithms\nin the field of NST. Then, we present several evaluation methods and compare\ndifferent NST algorithms both qualitatively and quantitatively. The review\nconcludes with a discussion of various applications of NST and open problems\nfor future research. A list of papers discussed in this review, corresponding\ncodes, pre-trained models and more comparison results are publicly available at\nhttps://github.com/ycjing/Neural-Style-Transfer-Papers.","url_abs":"http://arxiv.org/abs/1705.04058v7","url_pdf":"http://arxiv.org/pdf/1705.04058v7.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":"neural-style-transfer-a-review","repo_url":"https://github.com/ycjing/Neural-Style-Transfer-Papers","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"neural-style-transfer-a-review","repo_url":"https://github.com/akanametov/NeuralStyleTransfer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"neural-style-transfer-a-review","repo_url":"https://github.com/akanametov/neural-style-transfer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"neural-style-transfer-a-review","repo_url":"https://github.com/andy-yangz/writing_style_transfer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"neural-style-transfer-a-review","repo_url":"https://github.com/ryanchankh/style_transfer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"neural-style-transfer-a-review","repo_url":"https://github.com/taylorjocelyn/diffusion-model-quantization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"torch","reach":{"status":"unanswered"}},{"paper_slug":"neural-style-transfer-a-review","repo_url":"https://github.com/ycjing/Character-Stylization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"neural-style-transfer-a-review","repo_url":"https://github.com/yotharit/image_style_transfer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"neural-style-transfer-a-review","repo_url":"https://github.com/sonnguyen129/deep-feature-rotation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"style-transfer","task_name":"Style Transfer"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1705.04058","atlas_url":"https://app.syntology.ai/?focus=1705.04058","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}