{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T19:12:36Z","timestamp":1764270756008,"version":"3.46.0"},"reference-count":51,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001348","name":"Agency for Science, Technology and Research (A*STAR) Centre for Frontier AI Research, the A*STAR GAP project","doi-asserted-by":"publisher","award":["I23D1AG079"],"award-info":[{"award-number":["I23D1AG079"]}],"id":[{"id":"10.13039\/501100001348","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"publisher","award":["62250710682"],"award-info":[{"award-number":["62250710682"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Program for Guangdong Provincial Key Laboratory","award":["2020B121201001"],"award-info":[{"award-number":["2020B121201001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Artif. Intell."],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1109\/tai.2024.3497915","type":"journal-article","created":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T14:04:52Z","timestamp":1731506692000},"page":"816-828","source":"Crossref","is-referenced-by-count":0,"title":["Generation With Nuanced Changes: Continuous Image-to-Image Translation With Adversarial Preferences"],"prefix":"10.1109","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3204-0739","authenticated-orcid":false,"given":"Yinghua","family":"Yao","sequence":"first","affiliation":[{"name":"Centre for Frontier AI Research, Agency for Science, Technology and Research (A*STAR), Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7950-4900","authenticated-orcid":false,"given":"Yuangang","family":"Pan","sequence":"additional","affiliation":[{"name":"Centre for Frontier AI Research, Agency for Science, Technology and Research (A*STAR), Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2211-8176","authenticated-orcid":false,"given":"Ivor W.","family":"Tsang","sequence":"additional","affiliation":[{"name":"Centre for Frontier AI Research, Agency for Science, Technology and Research (A*STAR), Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8837-4442","authenticated-orcid":false,"given":"Xin","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Data Science, Lingnan University, Hong Kong"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2020.3031581"},{"key":"ref2","first-page":"1964","article-title":"Breaking the dilemma of medical image-to-image translation","volume":"34","author":"Kong","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2022.3187384"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00194"},{"key":"ref7","article-title":"Diffusion-based image translation using disentangled style and content representation","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kwon","year":"2023"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3308102"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-021-01557-6"},{"key":"ref10","first-page":"5967","article-title":"Fader networks: Manipulating images by sliding attributes","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lample","year":"2017"},{"key":"ref11","article-title":"Latent space factorisation and manipulation via matrix subspace projection","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li","year":"2020"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00794"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00818"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2916751"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2950198"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126281"},{"key":"ref17","first-page":"5914","article-title":"RelGAN: Multi-domain image-to-image translation via relative attributes","volume-title":"Proc. IEEE Int. Conf. Comput. Vis.","author":"Wu","year":"2019"},{"key":"ref18","first-page":"131","article-title":"Ranking CGANs: Subjective control over semantic image attributes","volume-title":"Proc. Brit. Mach. Vis. Conf. (BMVC)","author":"Saquil","year":"2018"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"ref20","first-page":"2590","article-title":"A unified feature disentangler for multi-domain image translation and manipulation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Liu","year":"2018"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i3.28012"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01036"},{"key":"ref23","article-title":"Understanding and improving interpolation in autoencoders via an adversarial regularizer","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Berthelot","year":"2018"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00251"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00926"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00577"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00182"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.02113"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01585"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3450626.3459838"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00520"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00824"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00518"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01249-6_50"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58604-1_3"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00205"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00916"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/1148170.1148205"},{"issue":"119","key":"ref39","first-page":"1","article-title":"Generative adversarial ranking nets","volume":"25","author":"Yao","year":"2024","journal-title":"J. Mach. Learn. Res."},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1606.01583"},{"key":"ref41","first-page":"1369","article-title":"An analysis of inference with the universum","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Chapelle","year":"2008"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/1390334.1390382"},{"key":"ref43","first-page":"1614","article-title":"From softmax to sparsemax: A sparse model of attention and multi-label classification","volume-title":"Proc. Int. Conf. Mach. Learn. (PMLR)","author":"Martins","year":"2016"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.304"},{"key":"ref45","article-title":"Large scale GAN training for high fidelity natural image synthesis","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Brock","year":"2019"},{"key":"ref46","article-title":"Improved training of wasserstein GANs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Gulrajani","year":"2017"},{"key":"ref47","article-title":"Progressive growing of gans for improved quality, stability, and variation","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Karras","year":"2018"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.32"},{"key":"ref50","article-title":"Mixup: Beyond empirical risk minimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhang","year":"2018"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["IEEE Transactions on Artificial Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/9078688\/10946214\/10752923.pdf?arnumber=10752923","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T19:01:09Z","timestamp":1764270069000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10752923\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":51,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tai.2024.3497915","relation":{},"ISSN":["2691-4581"],"issn-type":[{"type":"electronic","value":"2691-4581"}],"subject":[],"published":{"date-parts":[[2025,4]]}}}