{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T19:33:06Z","timestamp":1775503986360,"version":"3.50.1"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,12,31]],"date-time":"2022-12-31T00:00:00Z","timestamp":1672444800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,31]],"date-time":"2022-12-31T00:00:00Z","timestamp":1672444800000},"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":["Neural Process Lett"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s11063-022-11135-7","type":"journal-article","created":{"date-parts":[[2022,12,31]],"date-time":"2022-12-31T03:02:43Z","timestamp":1672455763000},"page":"6213-6230","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Adaptive Style Modulation for Artistic Style Transfer"],"prefix":"10.1007","volume":"55","author":[{"given":"Yipeng","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingliang","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingying","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chi","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,31]]},"reference":[{"key":"11135_CR1","doi-asserted-by":"crossref","unstructured":"Gooch B, Gooch A (2001) Non-photorealistic rendering. AK Peters\/CRC Press","DOI":"10.1201\/9781439864173"},{"key":"11135_CR2","unstructured":"Strothotte T, Schlechtweg S (2002) Non-photorealistic computer graphics: modeling, rendering, and animation. Morgan Kaufmann Publishers Inc"},{"key":"11135_CR3","doi-asserted-by":"crossref","unstructured":"Rosin P, Collomosse J (2012) Image and video-based artistic stylisation. Springer","DOI":"10.1007\/978-1-4471-4519-6"},{"issue":"11","key":"11135_CR4","doi-asserted-by":"publisher","first-page":"3365","DOI":"10.1109\/TVCG.2019.2921336","volume":"26","author":"Y Jing","year":"2019","unstructured":"Jing Y, Yang Y, Feng Z, Ye J, Yu Y, Song M (2019) Neural style transfer: a review. IEEE Trans Vis Comput Graph 26(11):3365\u20133385","journal-title":"IEEE Trans Vis Comput Graph"},{"issue":"1\u20133","key":"11135_CR5","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/j.neucom.2010.03.021","volume":"74","author":"J Misra","year":"2010","unstructured":"Misra J, Saha I (2010) Artificial neural networks in hardware: a survey of two decades of progress. Neurocomputing 74(1\u20133):239\u2013255","journal-title":"Neurocomputing"},{"key":"11135_CR6","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.neunet.2018.10.004","volume":"109","author":"Y Cao","year":"2019","unstructured":"Cao Y, Cao Y, Wen S, Huang T, Zeng Z (2019) Passivity analysis of delayed reaction-diffusion memristor-based neural networks. Neural Netw 109:159\u2013167","journal-title":"Neural Netw"},{"key":"11135_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109595","volume":"254","author":"Y Cao","year":"2022","unstructured":"Cao Y, Liu N, Zhang C, Zhang T, Luo Z-F (2022) Synchronization of multiple reaction-diffusion memristive neural networks with known or unknown parameters and switching topologies. Knowl Based Syst 254:109595","journal-title":"Knowl Based Syst"},{"issue":"2","key":"11135_CR8","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1038\/s41928-018-0023-2","volume":"1","author":"Z Wang","year":"2018","unstructured":"Wang Z, Joshi S, Savel\u2019ev S, Song W, Midya R, Li Y, Rao M, Yan P, Asapu S, Zhuo Y et al (2018) Fully memristive neural networks for pattern classification with unsupervised learning. Nat Electron 1(2):137\u2013145","journal-title":"Nat Electron"},{"issue":"7","key":"11135_CR9","doi-asserted-by":"publisher","first-page":"881","DOI":"10.1016\/j.neunet.2010.05.001","volume":"23","author":"YV Pershin","year":"2010","unstructured":"Pershin YV, Di Ventra M (2010) Experimental demonstration of associative memory with memristive neural networks. Neural Netw 23(7):881\u2013886","journal-title":"Neural Netw"},{"issue":"6","key":"11135_CR10","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390","journal-title":"Commun ACM"},{"key":"11135_CR11","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp 448\u2013456 . PMLR"},{"key":"11135_CR12","unstructured":"Ba JL, Kiros JR, Hinton GE (2016) Layer normalization. arXiv preprint arXiv:1607.06450"},{"key":"11135_CR13","unstructured":"Xu J, Sun X, Zhang Z, Zhao G, Lin J (2019) Understanding and improving layer normalization. In: Advances in neural information processing systems, vol 32, pp 4383\u20134393"},{"key":"11135_CR14","unstructured":"Salimans T, Kingma DP (2016) Weight normalization: a simple reparameterization to accelerate training of deep neural networks. In: Advances in neural information processing systems, vol 29, pp 901\u2013909"},{"key":"11135_CR15","doi-asserted-by":"crossref","unstructured":"Wu Y, He K (2018) Group normalization. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 3\u201319","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"11135_CR16","unstructured":"Ulyanov D, Vedaldi A, Lempitsky V (2016) Instance normalization: the missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022"},{"key":"11135_CR17","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 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"11135_CR18","first-page":"2899","volume":"10","author":"J Duchi","year":"2009","unstructured":"Duchi J, Singer Y (2009) Efficient online and batch learning using forward backward splitting. J Mach Learn Res 10:2899\u20132934","journal-title":"J Mach Learn Res"},{"key":"11135_CR19","unstructured":"Zeiler MD (2012) Adadelta: an adaptive learning rate method. arXiv preprint arXiv:1212.5701"},{"key":"11135_CR20","doi-asserted-by":"crossref","unstructured":"Zeiler MD, Taylor GW, Fergus R (2011) Adaptive deconvolutional networks for mid and high level feature learning. In: 2011 International Conference on Computer Vision, pp 2018\u20132025","DOI":"10.1109\/ICCV.2011.6126474"},{"key":"11135_CR21","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"issue":"6","key":"11135_CR22","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.1109\/TCAD.2018.2834436","volume":"38","author":"S Wen","year":"2018","unstructured":"Wen S, Xiao S, Yang Y, Yan Z, Zeng Z, Huang T (2018) Adjusting learning rate of memristor-based multilayer neural networks via fuzzy method. IEEE Trans Comput Aided Des Integr Circ Syst 38(6):1084\u20131094","journal-title":"IEEE Trans Comput Aided Des Integr Circ Syst"},{"key":"11135_CR23","doi-asserted-by":"crossref","unstructured":"Parikh AP, T\u00e4ckstr\u00f6m O, Das D, Uszkoreit J (2016) A decomposable attention model for natural language inference. arXiv preprint arXiv:1606.01933","DOI":"10.18653\/v1\/D16-1244"},{"key":"11135_CR24","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser, \u0141, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, vol 30, pp 5998\u20136008"},{"key":"11135_CR25","unstructured":"Zhang H, Goodfellow I, Metaxas D, Odena A (2019) Self-attention generative adversarial networks. In: International Conference on Machine Learning, pp 7354\u20137363 . PMLR"},{"key":"11135_CR26","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"11135_CR27","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"11135_CR28","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"11135_CR29","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-first AAAI Conference on Artificial Intelligence","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"11135_CR30","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861"},{"key":"11135_CR31","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhou X, Lin M, Sun J (2018) Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 6848\u20136856","DOI":"10.1109\/CVPR.2018.00716"},{"key":"11135_CR32","doi-asserted-by":"crossref","unstructured":"Sun K, Xiao B, Liu D, Wang J (2019) Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 5693\u20135703","DOI":"10.1109\/CVPR.2019.00584"},{"key":"11135_CR33","doi-asserted-by":"crossref","unstructured":"Gatys LA, Ecker AS, Bethge M (2016) Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2414\u20132423","DOI":"10.1109\/CVPR.2016.265"},{"key":"11135_CR34","doi-asserted-by":"crossref","unstructured":"Gatys L, Ecker AS, Bethge M (2015) Texture synthesis using convolutional neural networks. In: Advances in Neural Information Processing Systems, pp 262\u2013270","DOI":"10.1109\/CVPR.2016.265"},{"key":"11135_CR35","doi-asserted-by":"crossref","unstructured":"Johnson J, Alahi A, Fei-Fei L (2016) Perceptual losses for real-time style transfer and super-resolution. In: European Conference on Computer Vision, Springer, pp 694\u2013711","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"11135_CR36","unstructured":"Ulyanov D, Lebedev V, Vedaldi A, Lempitsky VS (2016) Texture networks: feed-forward synthesis of textures and stylized images. In: ICML, p 4"},{"key":"11135_CR37","doi-asserted-by":"crossref","unstructured":"Ulyanov D, Vedaldi A, Lempitsky V (2017) Improved texture networks: maximizing quality and diversity in feed-forward stylization and texture synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 6924\u20136932","DOI":"10.1109\/CVPR.2017.437"},{"key":"11135_CR38","doi-asserted-by":"crossref","unstructured":"Li C, Wand M (2016) Precomputed real-time texture synthesis with markovian generative adversarial networks. In: European Conference on Computer Vision, Springer, pp 702\u2013716","DOI":"10.1007\/978-3-319-46487-9_43"},{"key":"11135_CR39","unstructured":"Dumoulin V, Shlens J, Kudlur M (2016) A learned representation for artistic style. arXiv preprint arXiv:1610.07629"},{"key":"11135_CR40","doi-asserted-by":"crossref","unstructured":"Huang X, Belongie S (2017) Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1501\u20131510","DOI":"10.1109\/ICCV.2017.167"},{"key":"11135_CR41","unstructured":"Li Y, Fang C, Yang J, Wang Z, Lu X, Yang M-H (2017) Universal style transfer via feature transforms. In: Advances in Neural Information Processing Systems, pp 386\u2013396"},{"key":"11135_CR42","doi-asserted-by":"crossref","unstructured":"Li X, Liu S, Kautz J, Yang M.-H (2019) Learning linear transformations for fast image and video style transfer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3809\u20133817","DOI":"10.1109\/CVPR.2019.00393"},{"key":"11135_CR43","doi-asserted-by":"crossref","unstructured":"Sheng L, Lin Z, Shao J, Wang X (2018) Avatar-net: multi-scale zero-shot style transfer by feature decoration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 8242\u20138250","DOI":"10.1109\/CVPR.2018.00860"},{"key":"11135_CR44","doi-asserted-by":"crossref","unstructured":"Park DY, Lee KH (2019) Arbitrary style transfer with style-attentional networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 5880\u20135888","DOI":"10.1109\/CVPR.2019.00603"},{"key":"11135_CR45","doi-asserted-by":"crossref","unstructured":"Deng Y, Tang F, Dong W, Sun W, Huang F, Xu C (2020) Arbitrary style transfer via multi-adaptation network. In: Proceedings of the 28th ACM International Conference on Multimedia, pp 2719\u20132727","DOI":"10.1145\/3394171.3414015"},{"key":"11135_CR46","doi-asserted-by":"crossref","unstructured":"Yao Y, Ren J, Xie X, Liu W, Liu Y-J, Wang J (2019) Attention-aware multi-stroke style transfer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 1467\u20131475","DOI":"10.1109\/CVPR.2019.00156"},{"key":"11135_CR47","first-page":"26561","volume":"34","author":"H Chen","year":"2021","unstructured":"Chen H, Wang Z, Zhang H, Zuo Z, Li A, Xing W, Lu D et al (2021) Artistic style transfer with internal-external learning and contrastive learning. Adv Neural Inf Process Syst 34:26561\u201326573","journal-title":"Adv Neural Inf Process Syst"},{"key":"11135_CR48","doi-asserted-by":"crossref","unstructured":"Ghiasi G, Lee H, Kudlur M, Dumoulin V, Shlens J (2017) Exploring the structure of a real-time, arbitrary neural artistic stylization network. In: Proceedings of the British machine vision conference, pp 114.1\u2013114.12","DOI":"10.5244\/C.31.114"},{"key":"11135_CR49","doi-asserted-by":"crossref","unstructured":"Park DY, Lee KH (2019) Arbitrary style transfer with style-attentional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5880\u20135888","DOI":"10.1109\/CVPR.2019.00603"},{"key":"11135_CR50","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 stylegan. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 8110\u20138119","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"11135_CR51","first-page":"852","volume":"34","author":"T Karras","year":"2021","unstructured":"Karras T, Aittala M, Laine S, H\u00e4rk\u00f6nen E, Hellsten J, Lehtinen J, Aila T (2021) Alias-free generative adversarial networks. Adv Neural Inf Process Syst 34:852\u2013863","journal-title":"Adv Neural Inf Process Syst"},{"key":"11135_CR52","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 Conference on Computer Vision and Pattern Recognition, pp 8188\u20138197","DOI":"10.1109\/CVPR42600.2020.00821"},{"key":"11135_CR53","doi-asserted-by":"crossref","unstructured":"Huang X, Liu M-Y, Belongie S, Kautz J (2018) Multimodal unsupervised image-to-image translation. In: Proceedings of the European Conference on Computer Vision, pp 172\u2013189","DOI":"10.1007\/978-3-030-01219-9_11"},{"key":"11135_CR54","doi-asserted-by":"crossref","unstructured":"Wang Y, Gonzalez-Garcia A, van\u00a0de Weijer J, Herranz L (2019) Sdit: scalable and diverse cross-domain image translation. In: Proceedings of the 27th ACM International Conference on Multimedia, pp 1267\u20131276","DOI":"10.1145\/3343031.3351004"},{"key":"11135_CR55","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 Conference on Computer Vision and Pattern Recognition, pp 4401\u20134410","DOI":"10.1109\/CVPR.2019.00453"},{"key":"11135_CR56","doi-asserted-by":"crossref","unstructured":"Li Y, Wang N, Liu J, Hou X (2017) Demystifying neural style transfer. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp 2230\u20132236","DOI":"10.24963\/ijcai.2017\/310"},{"key":"11135_CR57","doi-asserted-by":"crossref","unstructured":"Chandran P, Zoss G, Gotardo P, Gross M, Bradley D (2021) Adaptive convolutions for structure-aware style transfer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 7972\u20137981","DOI":"10.1109\/CVPR46437.2021.00788"},{"key":"11135_CR58","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft coco: common objects in context. In: European Conference on Computer Vision, pp 740\u2013755 . Springer","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"11135_CR59","unstructured":"Wikiart P (2016) www.kaggle.com\/c\/painter-by-numbers"},{"key":"11135_CR60","doi-asserted-by":"crossref","unstructured":"Li X, Liu S, Kautz J, Yang M-H (2019) Learning linear transformations for fast image and video style transfer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 3809\u20133817","DOI":"10.1109\/CVPR.2019.00393"},{"key":"11135_CR61","doi-asserted-by":"crossref","unstructured":"Sheng L, Lin Z, Shao J, Wang X (2018) Avatar-net: multi-scale zero-shot style transfer by feature decoration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 8242\u20138250","DOI":"10.1109\/CVPR.2018.00860"},{"key":"11135_CR62","doi-asserted-by":"crossref","unstructured":"Liu S, Lin T, He D, Li F, Wang M, Li X, Sun Z, Li Q, Ding E (2021) Adaattn: Revisit attention mechanism in arbitrary neural style transfer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 6649\u20136658","DOI":"10.1109\/ICCV48922.2021.00658"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-11135-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-022-11135-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-11135-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T16:17:19Z","timestamp":1696004239000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-022-11135-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,31]]},"references-count":62,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["11135"],"URL":"https:\/\/doi.org\/10.1007\/s11063-022-11135-7","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,31]]},"assertion":[{"value":"18 December 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 December 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}