{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T15:41:27Z","timestamp":1743090087813,"version":"3.40.3"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031208676"},{"type":"electronic","value":"9783031208683"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20868-3_22","type":"book-chapter","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T23:29:12Z","timestamp":1667518152000},"page":"296-309","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["CrGAN: Continuous Rendering of\u00a0Image Style"],"prefix":"10.1007","author":[{"given":"Xiaoming","family":"Yu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gan","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,4]]},"reference":[{"issue":"3","key":"22_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3447648","volume":"40","author":"R Abdal","year":"2021","unstructured":"Abdal, R., Zhu, P., Mitra, N.J., Wonka, P.: StyleFlow: attribute-conditioned exploration of styleGAN-generated images using conditional continuous normalizing flows. TOG 40(3), 1\u201321 (2021)","journal-title":"TOG"},{"doi-asserted-by":"crossref","unstructured":"Anokhin, I., et al.: High-resolution daytime translation without domain labels. In: CVPR, pp. 7488\u20137497 (2020)","key":"22_CR2","DOI":"10.1109\/CVPR42600.2020.00751"},{"key":"22_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1007\/978-3-030-58598-3_34","volume-title":"Computer Vision \u2013 ECCV 2020","author":"H-Y Chang","year":"2020","unstructured":"Chang, H.-Y., Wang, Z., Chuang, Y.-Y.: Domain-specific mappings for generative adversarial style transfer. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 573\u2013589. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58598-3_34"},{"doi-asserted-by":"crossref","unstructured":"Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., Choo, J.: StarGAN: unified generative adversarial networks for multi-domain image-to-image translation. In: CVPR, pp. 8789\u20138797 (2018)","key":"22_CR4","DOI":"10.1109\/CVPR.2018.00916"},{"doi-asserted-by":"crossref","unstructured":"Choi, Y., Uh, Y., Yoo, J., Ha, J.W.: StarGAN V2: diverse image synthesis for multiple domains. In: CVPR, pp. 8188\u20138197 (2020)","key":"22_CR5","DOI":"10.1109\/CVPR42600.2020.00821"},{"doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: CVPR, pp. 3213\u20133223 (2016)","key":"22_CR6","DOI":"10.1109\/CVPR.2016.350"},{"unstructured":"Dumoulin, V., Shlens, J., Kudlur, M.: A learned representation for artistic style. ICLR (2017)","key":"22_CR7"},{"doi-asserted-by":"crossref","unstructured":"Gabbay, A., Hoshen, Y.: Improving style-content disentanglement in image-to-image translation. arXiv preprint arXiv:2007.04964 (2020)","key":"22_CR8","DOI":"10.1109\/ICCV48922.2021.00671"},{"doi-asserted-by":"crossref","unstructured":"Gong, R., Li, W., Chen, Y., Gool, L.V.: DLOW: domain flow for adaptation and generalization. In: CVPR, pp. 2477\u20132486 (2019)","key":"22_CR9","DOI":"10.1109\/CVPR.2019.00258"},{"unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local nash equilibrium. In: NeurIPS, vol. 30 (2017)","key":"22_CR10"},{"doi-asserted-by":"crossref","unstructured":"Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: ICCV, pp. 1501\u20131510 (2017)","key":"22_CR11","DOI":"10.1109\/ICCV.2017.167"},{"key":"22_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/978-3-030-01219-9_11","volume-title":"Computer Vision \u2013 ECCV 2018","author":"X Huang","year":"2018","unstructured":"Huang, X., Liu, M.-Y., Belongie, S., Kautz, J.: Multimodal unsupervised image-to-image translation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 179\u2013196. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01219-9_11"},{"doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: CVPR, pp. 1125\u20131134 (2017)","key":"22_CR13","DOI":"10.1109\/CVPR.2017.632"},{"key":"22_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1007\/978-3-319-46475-6_43","volume-title":"Computer Vision \u2013 ECCV 2016","author":"J Johnson","year":"2016","unstructured":"Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694\u2013711. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_43"},{"doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: CVPR, pp. 4401\u20134410 (2019)","key":"22_CR15","DOI":"10.1109\/CVPR.2019.00453"},{"doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of styleGAN. In: CVPR, pp. 8110\u20138119 (2020)","key":"22_CR16","DOI":"10.1109\/CVPR42600.2020.00813"},{"unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)","key":"22_CR17"},{"key":"22_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1007\/978-3-030-01246-5_3","volume-title":"Computer Vision \u2013 ECCV 2018","author":"H-Y Lee","year":"2018","unstructured":"Lee, H.-Y., Tseng, H.-Y., Huang, J.-B., Singh, M., Yang, M.-H.: Diverse image-to-image translation via disentangled representations. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 36\u201352. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01246-5_3"},{"doi-asserted-by":"crossref","unstructured":"Li, X., et al.: Image-to-image translation via hierarchical style disentanglement. In: CVPR, pp. 8639\u20138648 (2021)","key":"22_CR19","DOI":"10.1109\/CVPR46437.2021.00853"},{"doi-asserted-by":"crossref","unstructured":"Lin, J., Zhang, R., Ganz, F., Han, S., Zhu, J.Y.: Anycost GANs for interactive image synthesis and editing. In: CVPR, pp. 14986\u201314996 (2021)","key":"22_CR20","DOI":"10.1109\/CVPR46437.2021.01474"},{"doi-asserted-by":"crossref","unstructured":"Mao, Q., Lee, H.Y., Tseng, H.Y., Ma, S., Yang, M.H.: Mode seeking generative adversarial networks for diverse image synthesis. In: CVPR, pp. 1429\u20131437 (2019)","key":"22_CR21","DOI":"10.1109\/CVPR.2019.00152"},{"key":"22_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11263-021-01557-6","volume":"130","author":"Q Mao","year":"2022","unstructured":"Mao, Q., Tseng, H.Y., Lee, H.Y., Huang, J.B., Ma, S., Yang, M.H.: Continuous and diverse image-to-image translation via signed attribute vectors. IJCV 130, 1\u201333 (2022)","journal-title":"IJCV"},{"doi-asserted-by":"crossref","unstructured":"Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: ICCV, pp. 2794\u20132802 (2017)","key":"22_CR23","DOI":"10.1109\/ICCV.2017.304"},{"doi-asserted-by":"crossref","unstructured":"Pizzati, F., Cerri, P., de Charette, R.: CoMoGAN: continuous model-guided image-to-image translation. In: CVPR, pp. 14288\u201314298 (2021)","key":"22_CR24","DOI":"10.1109\/CVPR46437.2021.01406"},{"issue":"9","key":"22_CR25","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1007\/s11263-018-1072-8","volume":"126","author":"C Sakaridis","year":"2018","unstructured":"Sakaridis, C., Dai, D., Van Gool, L.: Semantic foggy scene understanding with synthetic data. IJCV 126(9), 973\u2013992 (2018)","journal-title":"IJCV"},{"unstructured":"Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. In: NeurIPS, vol. 29 (2016)","key":"22_CR26"},{"doi-asserted-by":"crossref","unstructured":"Shen, Y., Gu, J., Tang, X., Zhou, B.: Interpreting the latent space of GANs for semantic face editing. In: CVPR, pp. 9243\u20139252 (2020)","key":"22_CR27","DOI":"10.1109\/CVPR42600.2020.00926"},{"doi-asserted-by":"crossref","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.: Improved texture networks: maximizing quality and diversity in feed-forward stylization and texture synthesis. In: CVPR, pp. 6924\u20136932 (2017)","key":"22_CR28","DOI":"10.1109\/CVPR.2017.437"},{"doi-asserted-by":"crossref","unstructured":"Upchurch, P., et al.: Deep feature interpolation for image content changes. In: CVPR, pp. 7064\u20137073 (2017)","key":"22_CR29","DOI":"10.1109\/CVPR.2017.645"},{"doi-asserted-by":"crossref","unstructured":"Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional GANs. In: CVPR, pp. 8798\u20138807 (2018)","key":"22_CR30","DOI":"10.1109\/CVPR.2018.00917"},{"doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Dong, C., Tang, X., Loy, C.C.: Deep network interpolation for continuous imagery effect transition. In: CVPR, pp. 1692\u20131701 (2019)","key":"22_CR31","DOI":"10.1109\/CVPR.2019.00179"},{"unstructured":"Wu, P.W., Lin, Y.J., Chang, C.H., Chang, E.Y., Liao, S.W.: RelGAN: multi-domain image-to-image translation via relative attributes. In: ICCV, pp. 5914\u20135922 (2019)","key":"22_CR32"},{"unstructured":"Xiao, T., Hong, J., Ma, J.: DNA-GAN: learning disentangled representations from multi-attribute images. In: ICLR Workshops (2018)","key":"22_CR33"},{"unstructured":"Yu, X., Chen, Y., Liu, S., Li, T., Li, G.: Multi-mapping image-to-image translation via learning disentanglement. In: NeurIPS, vol. 32 (2019)","key":"22_CR34"},{"unstructured":"Yu, X., Ying, Z., Li, T., Liu, S., Li, G.: Multi-mapping image-to-image translation with central biasing normalization. arXiv preprint arXiv:1806.10050 (2018)","key":"22_CR35"},{"doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV, pp. 2223\u20132232 (2017)","key":"22_CR36","DOI":"10.1109\/ICCV.2017.244"},{"unstructured":"Zhuang, P., Koyejo, O., Schwing, A.G.: Enjoy your editing: controllable GANs for image editing via latent space navigation. ICLR (2021)","key":"22_CR37"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2022: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20868-3_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T23:42:00Z","timestamp":1667518920000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20868-3_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031208676","9783031208683"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20868-3_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"4 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shangai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pricai.org\/2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"432","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"91","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"39","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7-8","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"n\/a","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}