{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T20:38:40Z","timestamp":1771706320926,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p>Controlling the degree of stylization in the Neural Style Transfer (NST) is a little tricky since it usually needs hand-engineering on hyper-parameters. In this paper, we propose the first deep Reinforcement Learning (RL) based architecture that splits one-step style transfer into a step-wise process for the NST task. Our RL-based method tends to preserve more details and structures of the content image in early steps, and synthesize more style patterns in later steps. It is a user-easily-controlled style-transfer method. Additionally, as our RL-based model performs the stylization progressively, it is lightweight and has lower computational complexity than existing one-step Deep Learning (DL) based models. Experimental results demonstrate the effectiveness and robustness of our method.<\/jats:p>","DOI":"10.24963\/ijcai.2023\/12","type":"proceedings-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T04:31:30Z","timestamp":1691728290000},"page":"100-108","source":"Crossref","is-referenced-by-count":2,"title":["Controlling Neural Style Transfer with Deep Reinforcement Learning"],"prefix":"10.24963","author":[{"given":"Chengming","family":"Feng","sequence":"first","affiliation":[{"name":"Chengdu University of Information Technology, China"}]},{"given":"Jing","family":"Hu","sequence":"additional","affiliation":[{"name":"Chengdu University of Information Technology, China"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"University at Buffalo, SUNY, USA"}]},{"given":"Shu","family":"Hu","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, USA"}]},{"given":"Bin","family":"Zhu","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia, China"}]},{"given":"Xi","family":"Wu","sequence":"additional","affiliation":[{"name":"Chengdu University of Information Technology, China"}]},{"given":"Hongtu","family":"Zhu","sequence":"additional","affiliation":[{"name":"University of North Carolina at Chapel Hill, USA"}]},{"given":"Siwei","family":"Lyu","sequence":"additional","affiliation":[{"name":"University at Buffalo, SUNY, USA"}]}],"member":"10584","event":{"name":"Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}","theme":"Artificial Intelligence","location":"Macau, SAR China","acronym":"IJCAI-2023","number":"32","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2023,8,19]]},"end":{"date-parts":[[2023,8,25]]}},"container-title":["Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T04:31:57Z","timestamp":1691728317000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2023\/12"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2023\/12","relation":{},"subject":[],"published":{"date-parts":[[2023,8]]}}}