{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T14:00:54Z","timestamp":1743084054553,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031159305"},{"type":"electronic","value":"9783031159312"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-15931-2_30","type":"book-chapter","created":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T05:03:47Z","timestamp":1662440627000},"page":"358-370","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AMMUNIT: An Attention-Based Multimodal Multi-domain UNsupervised Image-to-Image Translation Framework"],"prefix":"10.1007","author":[{"given":"Lei","family":"Luo","sequence":"first","affiliation":[]},{"given":"William H.","family":"Hsu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,7]]},"reference":[{"key":"30_CR1","unstructured":"Wang, Y., Tao, X., Qi, X., Shen, X., Jia, J.: Image inpainting via generative multi-column convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 331\u2013340. Curran Associates Inc., Montr\u00e9al (2018)"},{"key":"30_CR2","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: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, pp. 8110\u20138119 (2020)","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"30_CR3","doi-asserted-by":"crossref","unstructured":"Wang, Z., Chen, J., Hoi, S.C.H.: Deep learning for image super-resolution: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 1 (2020)","DOI":"10.1109\/TPAMI.2021.3069908"},{"key":"30_CR4","doi-asserted-by":"crossref","unstructured":"Chen, Q-F., Koltun, V.: Photographic image synthesis with cascaded refinement networks. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1511\u20131520. IEEE, Honolulu (2017)","DOI":"10.1109\/ICCV.2017.168"},{"key":"30_CR5","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.-Y., Zhou, T.-H., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii, pp. 5967\u20135976 (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"30_CR6","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: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2242\u20132251. IEEE, Venice (2017)","DOI":"10.1109\/ICCV.2017.244"},{"key":"30_CR7","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.) Multimodal Unsupervised Image-to-Image Translation. LNCS, vol. 11207, pp. 179\u2013196. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01219-9_11"},{"key":"30_CR8","doi-asserted-by":"crossref","unstructured":"Choi, Y., Uh, Y-J., Yoo, J., Ha, J-W.: StarGAN v2: diverse image synthesis for multiple domains. in: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8185\u20138194. IEEE, Seattle (2020)","DOI":"10.1109\/CVPR42600.2020.00821"},{"key":"30_CR9","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"},{"key":"30_CR10","unstructured":"Goodfellow, I. J., et al.: Generative adversarial nets. In: Proceedings of the 27th International Conference on Neural Information Processing Systems, pp. 2672\u20132680. MIT Press, Montreal (2014)"},{"key":"30_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1007\/978-3-319-46454-1_36","volume-title":"Computer Vision \u2013 ECCV 2016","author":"J-Y Zhu","year":"2016","unstructured":"Zhu, J.-Y., Kr\u00e4henb\u00fchl, P., Shechtman, E., Efros, A.A.: Generative visual manipulation on the natural image manifold. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 597\u2013613. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46454-1_36"},{"key":"30_CR12","unstructured":"Denton, E.L., Chintala, s., Szlam, A., Fergus, B.: Deep generative image models using a Laplacian pyramid of adversarial networks. In: Proceedings of the 28th International Conference on Neural Information Processing Systems, pp. 1486\u2013149. MIT Press, Montreal (2015)"},{"key":"30_CR13","unstructured":"Yang, J.-W., Kannan, A., Batra, D., Parikh, D.: LR-GAN: layered recursive generative adversarial networks for image generation. In: 5th International Conference on Learning Representations (ICLR), OpenReview.net, Toulon, France (2017)"},{"key":"30_CR14","unstructured":"Zhao, T., Mathieu, M., LeCun, Y.: Energy-based generative adversarial networks. In: 5th International Conference on Learning Representations (ICLR), OpenReview.net, Toulon, France (2017)"},{"key":"30_CR15","unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: Proceedings of the 34th International Conference on Machine Learning (ICML), pp. 214\u2013223. PMLR, Stockholm (2017)"},{"key":"30_CR16","unstructured":"Kim, T., Cha, M., Kim, H., Lee, J.-K., Kim, J.: Learning to discover cross-domain relations with generative adversarial networks. In: Proceedings of the 34th International Conference on Machine Learning (ICML), pp. 1857\u20131865. PMLR, Sydney (2017)"},{"key":"30_CR17","doi-asserted-by":"crossref","unstructured":"Yi, Z.-L., Zhang, H., Tan, P., Gong, M.-L.: DualGAN: unsupervised dual learning for image-to-image translation. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2868\u20132876. IEEE, Venice (2017)","DOI":"10.1109\/ICCV.2017.310"},{"key":"30_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/978-3-030-01264-9_10","volume-title":"Computer Vision \u2013 ECCV 2018","author":"B Zhao","year":"2018","unstructured":"Zhao, B., Chang, B., Jie, Z., Sigal, L.: Modular generative adversarial networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision \u2013 ECCV 2018. LNCS, vol. 11218, pp. 157\u2013173. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01264-9_10"},{"key":"30_CR19","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: 3rd International Conference on Learning Representations (ICLR), OpenReview.net, San Diego, CA, USA (2015)"},{"key":"30_CR20","doi-asserted-by":"crossref","unstructured":"Yang, Z.-C., He, X.-D., Gao, J.-F., Deng, L., Smola, A.-J.: Stacked attention networks for image question answering. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), pp. 21\u201329. IEEE, Las Vegas (2016)","DOI":"10.1109\/CVPR.2016.10"},{"key":"30_CR21","unstructured":"Xu, K., et al.: Show, attend and tell: Neural image caption generation with visual attention. In: Proceedings of the 32nd International Conference on Machine Learning (ICML), Lille, France, pp. 2048\u20132057 (2015)"},{"key":"30_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1007\/978-3-030-01261-8_34","volume-title":"Computer Vision \u2013 ECCV 2018","author":"X Liang","year":"2018","unstructured":"Liang, X., Zhang, H., Lin, L., Xing, E.: Generative semantic manipulation with mask-contrasting GAN. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 574\u2013590. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01261-8_34"},{"key":"30_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1007\/978-3-030-01216-8_11","volume-title":"Computer Vision \u2013 ECCV 2018","author":"X Chen","year":"2018","unstructured":"Chen, X., Xu, C., Yang, X., Tao, D.: Attention-GAN for object transfiguration in wild images. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 167\u2013184. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01216-8_11"},{"key":"30_CR24","doi-asserted-by":"crossref","unstructured":"Kastaniotis, D., Ntinou, I., Tsourounis, D., Economou, G., Fotopoulos, S.: Attention-aware generative adversarial networks (ATA-GANs). In 13th IEEE Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), Aristi Village, Zagorochoria, Greece, pp. 1\u20135 (2018)","DOI":"10.1109\/IVMSPW.2018.8448850"},{"key":"30_CR25","doi-asserted-by":"crossref","unstructured":"Tang, H., Xu, D., Sebe, N., Yan, Y.: Attention-guided generative adversarial networks for unsupervised image-to-image translation. In: International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, pp. 1\u20138 (2019)","DOI":"10.1109\/IJCNN.2019.8851881"},{"key":"30_CR26","doi-asserted-by":"crossref","unstructured":"He, K.-M., Zhang, X.-Y., Ren, S.-Q., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778. IEEE, Las Vegas (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"30_CR27","doi-asserted-by":"crossref","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.S.: Improved texture networks: maximizing quality and diversity in feed-forward stylization and texture synthesis. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4105\u20134113. IEEE, Honolulu (2017)","DOI":"10.1109\/CVPR.2017.437"},{"key":"30_CR28","doi-asserted-by":"crossref","unstructured":"Huang, X., Belongie, S.-J.: Arbitrary style transfer in real-time with adaptive instance normalization. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 1510\u20131519. IEEE, Venice (2017)","DOI":"10.1109\/ICCV.2017.167"},{"key":"30_CR29","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Papandreou, P., Kokkinos, I., Murphy, K., Yuille, A.-L: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Pattern Anal. Mach. Intell. 834\u2013848 (2018)","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"30_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., Zitnick, C.L.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-15931-2_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T18:09:51Z","timestamp":1680631791000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-15931-2_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031159305","9783031159312"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-15931-2_30","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":"7 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bristol","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","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":"6 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"561","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":"255","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":"4","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":"45% - 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":"3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}