{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T17:58:10Z","timestamp":1772647090495,"version":"3.50.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Science and Technology Foundation of China","award":["61271361"],"award-info":[{"award-number":["61271361"]}]},{"name":"National Science and Technology Foundation of China","award":["61761046"],"award-info":[{"award-number":["61761046"]}]},{"name":"National Science and Technology Foundation of China","award":["62061049"],"award-info":[{"award-number":["62061049"]}]},{"name":"National Science and Technology Foundation of China","award":["52102382"],"award-info":[{"award-number":["52102382"]}]},{"name":"Key Project of Applied Basic Research Program of Yunnan Provincial Department of Science and Technology","award":["202001BB050043"],"award-info":[{"award-number":["202001BB050043"]}]},{"DOI":"10.13039\/501100018531","name":"Major Science and Technology Projects in Yunnan Province","doi-asserted-by":"publisher","award":["202002AD080001"],"award-info":[{"award-number":["202002AD080001"]}],"id":[{"id":"10.13039\/501100018531","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Reserve talents of young and middle-aged academic and technical leaders in Yunnan Province","award":["2019HB121"],"award-info":[{"award-number":["2019HB121"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s10489-023-05229-5","type":"journal-article","created":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T06:02:10Z","timestamp":1707199330000},"page":"2614-2630","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Dual-path hypernetworks of style and text for one-shot domain adaptation"],"prefix":"10.1007","volume":"54","author":[{"given":"Siqi","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanyuan","family":"Pu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengpeng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiuxia","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinjing","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yupan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,6]]},"reference":[{"key":"5229_CR1","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Adv Neural Inf Process Syst 27"},{"key":"5229_CR2","doi-asserted-by":"crossref","unstructured":"Zhou D, Zhang H, Li Q, Ma J, Xu X (2022) Coutfitgan: learning to synthesize compatible outfits supervised by silhouette masks and fashion styles. IEEE Trans Multimed","DOI":"10.1109\/TMM.2022.3185894"},{"key":"5229_CR3","doi-asserted-by":"crossref","unstructured":"Tang H, Liu H, Xu D, Torr PH, Sebe N (2021) Attentiongan: unpaired image-to-image translation using attention-guided generative adversarial networks. IEEE Trans Neural Netw Learn Syst","DOI":"10.1109\/TIP.2021.3109531"},{"key":"5229_CR4","doi-asserted-by":"crossref","unstructured":"Li X, Zhang S, Hu J, Cao L, Hong X, Mao X, Huang F, Wu Y, Ji R (2021) Image-to-image translation via hierarchical style disentanglement. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a08639\u20138648","DOI":"10.1109\/CVPR46437.2021.00853"},{"key":"5229_CR5","doi-asserted-by":"crossref","unstructured":"Karras T, Laine S, Aittala M, Hellsten J, Lehtinen J, Aila T (2020) Analyzing and improving the image quality of style GAN. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a08110\u20138119","DOI":"10.1109\/CVPR42600.2020.00813"},{"issue":"11","key":"5229_CR6","doi-asserted-by":"publisher","first-page":"12704","DOI":"10.1007\/s10489-021-03064-0","volume":"52","author":"NK Yadav","year":"2022","unstructured":"Yadav NK, Singh SK, Dubey SR (2022) Csa-gan: cyclic synthesized attention guided generative adversarial network for face synthesis. Appl Intell 52(11):12704\u201312723","journal-title":"Appl Intell"},{"key":"5229_CR7","doi-asserted-by":"crossref","unstructured":"Zhang L, Long C, Yan Q, Zhang X, Xiao C (2020) Cla-GAN: a context and lightness aware generative adversarial network for shadow removal. In: Computer graphics forum vol\u00a039. Wiley Online Library, pp\u00a0483\u2013494","DOI":"10.1111\/cgf.14161"},{"key":"5229_CR8","doi-asserted-by":"crossref","unstructured":"Chen G, Zhang G, Yang Z, Liu W (2022) Multi-scale patch-gan with edge detection for image inpainting. Appl Intell 1\u201316","DOI":"10.1007\/s10489-022-03577-2"},{"key":"5229_CR9","doi-asserted-by":"crossref","unstructured":"Wang Y, Gonzalez-Garcia A, Berga D, Herranz L, Khan FS, Weijer Jvd (2020) Mine GAN: effective knowledge transfer from GANs to target domains with few images. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a09332\u20139341","DOI":"10.1109\/CVPR42600.2020.00935"},{"key":"5229_CR10","unstructured":"Mo S, Cho M, Shin J (2020) Freeze the discriminator: a simple baseline for fine-tuning gans. In: CVPR AI for content creation workshop"},{"key":"5229_CR11","first-page":"12104","volume":"33","author":"T Karras","year":"2020","unstructured":"Karras T, Aittala M, Hellsten J, Laine S, Lehtinen J, Aila T (2020) Training generative adversarial networks with limited data. Adv Neural Inf Process Syst 33:12104\u201312114","journal-title":"Adv Neural Inf Process Syst"},{"key":"5229_CR12","doi-asserted-by":"publisher","first-page":"1882","DOI":"10.1109\/TIP.2021.3049346","volume":"30","author":"N-T Tran","year":"2021","unstructured":"Tran N-T, Tran V-H, Nguyen N-B, Nguyen T-K, Cheung N-M (2021) On data augmentation for GAN training. IEEE Trans Image Process 30:1882\u20131897","journal-title":"IEEE Trans Image Process"},{"key":"5229_CR13","unstructured":"Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J et al. (2021) Learning transferable visual models from natural language supervision. In: International conference on machine learning, PMLR, pp\u00a08748\u20138763"},{"issue":"4","key":"5229_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3528223.3530164","volume":"41","author":"R Gal","year":"2022","unstructured":"Gal R, Patashnik O, Maron H, Bermano AH, Chechik G, Cohen-Or D (2022) Stylegan-nada: clip-guided domain adaptation of image generators. ACM Trans Graphics (TOG) 41(4):1\u201313","journal-title":"ACM Trans Graphics (TOG)"},{"key":"5229_CR15","doi-asserted-by":"crossref","unstructured":"Kim G, Kwon T, Ye JC (2022) Diffusionclip: text-guided diffusion models for robust image manipulation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a02426\u20132435","DOI":"10.1109\/CVPR52688.2022.00246"},{"key":"5229_CR16","unstructured":"Zhu P, Abdal R, Femiani J, Wonka P (2022) Mind the gap: domain gap control for single shot domain adaptation for generative adversarial networks"},{"key":"5229_CR17","doi-asserted-by":"crossref","unstructured":"Tan Z, Chai M, Chen D, Liao J, Chu Q, Liu B, Hua G, Yu N (2021) Diverse semantic image synthesis via probability distribution modeling. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a07962\u20137971","DOI":"10.1109\/CVPR46437.2021.00787"},{"key":"5229_CR18","doi-asserted-by":"crossref","unstructured":"Park S, Yoo C-H, Shin Y-G (2022) Effective shortcut technique for generative adversarial networks. Appl Intell 1\u201313","DOI":"10.1007\/s10489-022-03666-2"},{"key":"5229_CR19","unstructured":"Ansari AF, Scarlett J, Soh H (2020) A characteristic function approach to deep implicit generative modeling. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a07478\u20137487"},{"key":"5229_CR20","doi-asserted-by":"crossref","unstructured":"Tao S, Wang J (2020) Alleviation of gradient exploding in gans: fake can be real. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a01191\u20131200","DOI":"10.1109\/CVPR42600.2020.00127"},{"key":"5229_CR21","unstructured":"Karras T, Aila T, Laine S, Lehtinen J (2018) Progressive growing of gans for improved quality, stability, and variation. In: International conference on learning representations"},{"key":"5229_CR22","doi-asserted-by":"crossref","unstructured":"Karras T, Laine S, Aila T (2019) A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a04401\u20134410","DOI":"10.1109\/CVPR.2019.00453"},{"key":"5229_CR23","doi-asserted-by":"crossref","unstructured":"Yang T, Ren P, Xie X, Zhang L (2021) GAN prior embedded network for blind face restoration in the wild. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a0672\u2013681","DOI":"10.1109\/CVPR46437.2021.00073"},{"issue":"3","key":"5229_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3447648","volume":"40","author":"R Abdal","year":"2021","unstructured":"Abdal R, Zhu P, Mitra NJ, Wonka P (2021) Styleflow: attribute-conditioned exploration of styleGAN-generated images using conditional continuous normalizing flows. ACM Trans Graph (TOG) 40(3):1\u201321","journal-title":"ACM Trans Graph (TOG)"},{"issue":"4","key":"5229_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3450626.3459771","volume":"40","author":"G Song","year":"2021","unstructured":"Song G, Luo L, Liu J, Ma W-C, Lai C, Zheng C, Cham T-J (2021) agilegan: stylizing portraits by inversion-consistent transfer learning. ACM Trans Graph (TOG) 40(4):1\u201313","journal-title":"ACM Trans Graph (TOG)"},{"key":"5229_CR26","doi-asserted-by":"crossref","unstructured":"Alaluf Y, Tov O, Mokady R, Gal R, Bermano A (2022) Hyperstyle: stylegan inversion with hypernetworks for real image editing. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a018511\u201318521","DOI":"10.1109\/CVPR52688.2022.01796"},{"key":"5229_CR27","doi-asserted-by":"crossref","unstructured":"Patashnik O, Wu Z, Shechtman E, Cohen-Or D, Lischinski D (2021) Styleclip: text-driven manipulation of styleGAN imagery. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp\u00a02085\u20132094","DOI":"10.1109\/ICCV48922.2021.00209"},{"key":"5229_CR28","doi-asserted-by":"crossref","unstructured":"Richardson E, Alaluf Y, Patashnik O, Nitzan Y, Azar Y, Shapiro S, Cohen-Or D (2021) Encoding in style: a stylegan encoder for image-to-image translation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a02287\u20132296","DOI":"10.1109\/CVPR46437.2021.00232"},{"key":"5229_CR29","unstructured":"Shen Y, Yang C, Tang X, Zhou B (2020) InterfaceGAN: interpreting the disentangled face representation learned by GANs. IEEE Trans Pattern Anal Mach Intell"},{"key":"5229_CR30","doi-asserted-by":"crossref","unstructured":"Shen Y, Zhou B (2021) Closed-form factorization of latent semantics in GANs. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a01532\u20131540","DOI":"10.1109\/CVPR46437.2021.00158"},{"key":"5229_CR31","doi-asserted-by":"crossref","unstructured":"Shi Y, Aggarwal D, Jain AK (2021) Lifting 2d stylegan for 3d-aware face generation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a06258\u20136266","DOI":"10.1109\/CVPR46437.2021.00619"},{"key":"5229_CR32","doi-asserted-by":"crossref","unstructured":"Tewari A, Elgharib M, Bharaj G, Bernard F, Seidel H-P, P\u00e9rez P, Zollhofer M, Theobalt C (2020) Stylerig: rigging styleGAN for 3d control over portrait images. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a06142\u20136151","DOI":"10.1109\/CVPR42600.2020.00618"},{"key":"5229_CR33","unstructured":"Qiao T, Zhang J, Xu D, Tao D () Mirrorgan: learning text-to-image generation by redescription. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a01505\u20131514"},{"key":"5229_CR34","doi-asserted-by":"crossref","unstructured":"Gafni O, Polyak A, Ashual O, Sheynin S, Parikh D, Taigman Y (2022) Make-a-scene: scene-based text-to-image generation with human priors. In: European conference on computer vision, Springer, pp\u00a089\u2013106","DOI":"10.1007\/978-3-031-19784-0_6"},{"key":"5229_CR35","doi-asserted-by":"crossref","unstructured":"Avrahami O, Hayes T, Gafni O, Gupta S, Taigman Y, Parikh D, Lischinski D, Fried O, Yin X (2023) spatext: spatio-textual representation for controllable image generation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a018370\u201318380","DOI":"10.1109\/CVPR52729.2023.01762"},{"key":"5229_CR36","doi-asserted-by":"crossref","unstructured":"Kim Y, Lee J, Kim J-H, Ha J-W, Zhu J-Y (2023) dense text-to-image generation with attention modulation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp\u00a07701\u20137711","DOI":"10.1109\/ICCV51070.2023.00708"},{"key":"5229_CR37","doi-asserted-by":"crossref","unstructured":"Wang Y, Wu C, Herranz L, van\u00a0de Weijer J, Gonzalez-Garcia A, Raducanu B (2018) Transferring GANs: generating images from limited data. In: Proceedings of the European conference on computer vision (ECCV), pp\u00a0218\u2013234","DOI":"10.1007\/978-3-030-01231-1_14"},{"key":"5229_CR38","doi-asserted-by":"crossref","unstructured":"Ojha U, Li Y, Lu J, Efros AA, Lee YJ, Shechtman E, Zhang R (2021) Few-shot image generation via cross-domain correspondence. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a010743\u201310752","DOI":"10.1109\/CVPR46437.2021.01060"},{"key":"5229_CR39","doi-asserted-by":"crossref","unstructured":"Lin J, Pang Y, Xia Y, Chen Z, Luo J (2020) TuiGAN: learning versatile image-to-image translation with two unpaired images. In: European conference on computer vision, Springer, pp\u00a018\u201335","DOI":"10.1007\/978-3-030-58548-8_2"},{"key":"5229_CR40","doi-asserted-by":"crossref","unstructured":"Shaham TR, Dekel T, Michaeli T (2019) SinGAN: Learning a generative model from a single natural image. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp\u00a04570\u20134580","DOI":"10.1109\/ICCV.2019.00467"},{"key":"5229_CR41","doi-asserted-by":"crossref","unstructured":"Kwon G, Ye JC (2023) One-shot adaptation of gan in just one clip. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2023.3283551"},{"key":"5229_CR42","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp\u00a0770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"5229_CR43","doi-asserted-by":"crossref","unstructured":"Choi Y, Uh Y, Yoo J, Ha J-W (2020) StarGAN v2: diverse image synthesis for multiple domains. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a08188\u20138197","DOI":"10.1109\/CVPR42600.2020.00821"},{"key":"5229_CR44","doi-asserted-by":"crossref","unstructured":"Krause J, Stark M, Deng J, Fei-Fei L (2013) 3d object representations for fine-grained categorization. In: Proceedings of the IEEE international conference on computer vision workshops, pp\u00a0554\u2013561","DOI":"10.1109\/ICCVW.2013.77"},{"key":"5229_CR45","unstructured":"Liu L, Jiang H, He P, Chen W, Liu X, Gao J, Han J (2020) On the variance of the adaptive learning rate and beyond. In: Proceedings of the eighth international conference on learning representations (ICLR 2020)"},{"key":"5229_CR46","unstructured":"Zhang M, Lucas J, Ba J, Hinton GE (2019) Lookahead optimizer: k steps forward, 1 step back. Adv Neural Inf Process Syst 32"},{"issue":"4","key":"5229_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3450626.3459838","volume":"40","author":"O Tov","year":"2021","unstructured":"Tov O, Alaluf Y, Nitzan Y, Patashnik O, Cohen-Or D (2021) Designing an encoder for styleGAN image manipulation. ACM Trans Graph (TOG) 40(4):1\u201314","journal-title":"ACM Trans Graph (TOG)"},{"key":"5229_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2021.103203","volume":"207","author":"Z Wang","year":"2021","unstructured":"Wang Z, Zhao L, Chen H, Zuo Z, Li A, Xing W, Lu D (2021) Evaluate and improve the quality of neural style transfer. Comput Vis Image Underst 207:103203","journal-title":"Comput Vis Image Underst"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-05229-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-05229-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-05229-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T20:41:05Z","timestamp":1710362465000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-05229-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2]]},"references-count":48,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["5229"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-05229-5","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2]]},"assertion":[{"value":"7 December 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 February 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration"}},{"value":"The authors have no relevant financial or non-financial interests to disclose. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The authors have no financial or proprietary interests in any material discussed in this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}