{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:04:55Z","timestamp":1772755495791,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T00:00:00Z","timestamp":1668643200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T00:00:00Z","timestamp":1668643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U20A20176"],"award-info":[{"award-number":["U20A20176"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072062"],"award-info":[{"award-number":["62072062"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62106026"],"award-info":[{"award-number":["62106026"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176027"],"award-info":[{"award-number":["62176027"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing","doi-asserted-by":"publisher","award":["cstc2022ycjh-bgzxm0031"],"award-info":[{"award-number":["cstc2022ycjh-bgzxm0031"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing","doi-asserted-by":"publisher","award":["cstc2021jcyj-msxmX0273"],"award-info":[{"award-number":["cstc2021jcyj-msxmX0273"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010031","name":"Postdoctoral Research Foundation of China","doi-asserted-by":"publisher","award":["2020M683243"],"award-info":[{"award-number":["2020M683243"]}],"id":[{"id":"10.13039\/501100010031","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014718","name":"Innovative Research Group Project of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["cstc2022ycjh-bgzxm00"],"award-info":[{"award-number":["cstc2022ycjh-bgzxm00"]}],"id":[{"id":"10.13039\/100014718","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Sichuan Science and Technology Program","award":["2021YFQ0056"],"award-info":[{"award-number":["2021YFQ0056"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s00521-022-08002-w","type":"journal-article","created":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T14:07:46Z","timestamp":1668694066000},"page":"6197-6206","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Generative adversarial networks with adaptive learning strategy for noise-to-image synthesis"],"prefix":"10.1007","volume":"35","author":[{"given":"Yan","family":"Gan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9439-4623","authenticated-orcid":false,"given":"Tao","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hangcheng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mao","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingliang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,17]]},"reference":[{"key":"8002_CR1","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M et al (2014) Generative adversarial nets. In: Advances in Neural Information Processing Systems (NeruIPS), pp 2672\u20132680"},{"key":"8002_CR2","doi-asserted-by":"crossref","unstructured":"Liu Z, Luo P, Wang X et al (2015) Deep learning face attributes in the wild. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp 3730\u20133738","DOI":"10.1109\/ICCV.2015.425"},{"key":"8002_CR3","unstructured":"Yu F, Seff A, Zhang Y et al (2015) LSUN: construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365"},{"issue":"9","key":"8002_CR4","doi-asserted-by":"publisher","first-page":"4519","DOI":"10.1007\/s00521-018-3691-y","volume":"32","author":"H Zhang","year":"2020","unstructured":"Zhang H, Sun Y, Liu L et al (2020) ClothingOut: a category-supervised GAN model for clothing segmentation and retrieval. Neural Comput Appl 32(9):4519\u20134530","journal-title":"Neural Comput Appl"},{"key":"8002_CR5","unstructured":"Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784"},{"key":"8002_CR6","doi-asserted-by":"crossref","unstructured":"Gan Y, Xiang T, Liu H et al (2021) Teacher-supervised generative adversarial networks. In: Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), pp 1\u20136","DOI":"10.1109\/ICME51207.2021.9428290"},{"key":"8002_CR7","doi-asserted-by":"publisher","unstructured":"Franci B, Grammatico S (2021) Training generative adversarial networks via stochastic Nash Games. IEEE Trans Neural Netw Learn Syst 1\u201310. https:\/\/doi.org\/10.1109\/TNNLS.2021.3105227","DOI":"10.1109\/TNNLS.2021.3105227"},{"key":"8002_CR8","doi-asserted-by":"crossref","unstructured":"Wu YL, Shuai HH, Tam ZR et al (2021) Gradient normalization for generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp 6373\u20136382","DOI":"10.1109\/ICCV48922.2021.00631"},{"key":"8002_CR9","doi-asserted-by":"crossref","unstructured":"Tseng HY, Jiang L, Liu C et al (2021) Regularizing generative adversarial networks under limited data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 7921\u20137931","DOI":"10.1109\/CVPR46437.2021.00783"},{"key":"8002_CR10","doi-asserted-by":"crossref","unstructured":"Liu R, Ge Y, Choi CL et al (2021) Divco: diverse conditional image synthesis via contrastive generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 16377\u201316386","DOI":"10.1109\/CVPR46437.2021.01611"},{"issue":"10","key":"8002_CR11","doi-asserted-by":"publisher","first-page":"4651","DOI":"10.1007\/s00521-020-05308-5","volume":"33","author":"G Liu","year":"2021","unstructured":"Liu G, Li X, Wei J (2021) Large-area damage image restoration algorithm based on generative adversarial network. Neural Comput Appl 33(10):4651\u20134661","journal-title":"Neural Comput Appl"},{"issue":"7","key":"8002_CR12","doi-asserted-by":"publisher","first-page":"4804","DOI":"10.1109\/TCSVT.2021.3121987","volume":"32","author":"X Tian","year":"2021","unstructured":"Tian X, Shao J, Ouyang D et al (2021) Uav-satellite view synthesis for cross-view geo-localization. IEEE Trans Circuits Syst Video Technol 32(7):4804\u20134815","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"8002_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2022.08.006","author":"Y Gan","year":"2022","unstructured":"Gan Y, Xiang T, Liu H et al (2022) Learning-aware feature denoising discriminator. Inf Fusion. https:\/\/doi.org\/10.1016\/j.inffus.2022.08.006","journal-title":"Inf Fusion"},{"key":"8002_CR14","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3147425","author":"Z Zheng","year":"2022","unstructured":"Zheng Z, Bin Y, Lu X et al (2022) Asynchronous generative adversarial network for asymmetric unpaired image-to-image translation. IEEE Trans Multimed. https:\/\/doi.org\/10.1109\/TMM.2022.3147425","journal-title":"IEEE Trans Multimed"},{"issue":"1","key":"8002_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3424116","volume":"17","author":"Y Yu","year":"2021","unstructured":"Yu Y, Srivastava A, Canales S (2021) Conditional LSTM-GAN for melody generation from lyrics. ACM Trans Multimed Comput Commun Appl 17(1):1\u201320","journal-title":"ACM Trans Multimed Comput Commun Appl"},{"key":"8002_CR16","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.neucom.2020.04.086","volume":"425","author":"Y Huang","year":"2021","unstructured":"Huang Y, Gao Y, Gan Y et al (2021) A new financial data forecasting model using genetic algorithm and long short-term memory network. Neurocomputing 425:207\u2013218","journal-title":"Neurocomputing"},{"key":"8002_CR17","unstructured":"Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434"},{"key":"8002_CR18","unstructured":"Karras T, Aila T, Laine S et al (2018) Progressive growing of GANs for improved quality, stability, and variation. In: Proceedings of the International Conference on Learning Representations (ICLR)"},{"key":"8002_CR19","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 (CVPR), pp 4401\u20134410","DOI":"10.1109\/CVPR.2019.00453"},{"key":"8002_CR20","doi-asserted-by":"crossref","unstructured":"Karras T, Laine S, Aittala M et al (2020) Analyzing and improving the image quality of StyleGAN. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 8110\u20138119","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"8002_CR21","unstructured":"Karras T, Aittala M, Hellsten J et al (2020) Training generative adversarial networks with limited data. In: Advances in Neural Information Processing Systems (NeurIPS), pp 12104\u201312114"},{"key":"8002_CR22","unstructured":"Karras T, Aittala M, Laine S et al (2021) Alias-free generative adversarial networks. In: Advances in Neural Information Processing Systems (NeurIPS), pp 852\u2013863"},{"key":"8002_CR23","unstructured":"Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In: Proceedings of the International Conference on Machine Learning (ICML), pp 214\u2013223"},{"issue":"12","key":"8002_CR24","doi-asserted-by":"publisher","first-page":"2947","DOI":"10.1109\/TPAMI.2018.2872043","volume":"41","author":"X Mao","year":"2018","unstructured":"Mao X, Li Q, Xie H et al (2018) On the effectiveness of least squares generative adversarial networks. IEEE Trans Pattern Anal Mach Intell 41(12):2947\u20132960","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8002_CR25","unstructured":"Gulrajani I, Ahmed F, Arjovsky M et al (2017) Improved training of Wasserstein GANs. In: Advances in Neural Information Processing Systems (NeurIPS), pp 5767\u20135777"},{"key":"8002_CR26","unstructured":"Zhang H, Zhang Z, Odena A et al (2020) Consistency regularization for generative adversarial networks. In: Proceedings of the International Conference on Learning Representations (ICLR)"},{"key":"8002_CR27","unstructured":"Odena A, Buckman J, Olsson C et al (2018) Is generator conditioning causally related to GAN performance? In: Proceedings of the International Conference on Machine Learning (ICML), pp 3849\u20133858"},{"key":"8002_CR28","unstructured":"Che T, Li Y, Jacob AP et al (2017) Mode regularized generative adversarial networks. In: Proceedings of the International Conference on Learning Representations (ICLR)"},{"key":"8002_CR29","unstructured":"Salimans T, Goodfellow I, Zaremba W et al (2016) Improved techniques for training GANs. In: Advances in Neural Information Processing Systems (NeurIPS), pp 2234\u20132242"},{"key":"8002_CR30","doi-asserted-by":"crossref","unstructured":"Pan X, Luo P, Shi J et al (2018) Two at once: enhancing learning and generalization capacities via IBN-net. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 464\u2013479","DOI":"10.1007\/978-3-030-01225-0_29"},{"key":"8002_CR31","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.ins.2018.01.027","volume":"436","author":"F Wang","year":"2018","unstructured":"Wang F, Zhang H, Li K et al (2018) A hybrid particle swarm optimization algorithm using adaptive learning strategy. Inf Sci 436:162\u2013177","journal-title":"Inf Sci"},{"key":"8002_CR32","first-page":"1","volume":"2022","author":"F Wang","year":"2022","unstructured":"Wang F, Xu Z, Ni W et al (2022) An adaptive learning image denoising algorithm based on eigenvalue extraction and the GAN model. Comput Intell Neurosci 2022:1\u201310","journal-title":"Comput Intell Neurosci"},{"key":"8002_CR33","unstructured":"Gan Z, Chen L, Wang W et al (2017) Triangle generative adversarial networks. In: Advances in Neural Information Processing Systems (NeurIPS), pp 1\u201310"},{"issue":"18","key":"8002_CR34","doi-asserted-by":"publisher","first-page":"14521","DOI":"10.1007\/s00521-019-04239-0","volume":"32","author":"M Zareapoor","year":"2020","unstructured":"Zareapoor M, Zhou H, Yang J (2020) Perceptual image quality using dual generative adversarial network. Neural Comput Appl 32(18):14521\u201314531","journal-title":"Neural Comput Appl"},{"key":"8002_CR35","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 (CVPR), pp 4401\u20134410","DOI":"10.1109\/CVPR.2019.00453"},{"key":"8002_CR36","unstructured":"Deng Z, Zhang H, Liang X et al (2017) Structured generative adversarial networks. In: Advances in Neural Information Processing Systems (NeurIPS), pp 1\u201311"},{"key":"8002_CR37","unstructured":"Zhang H, Goodfellow I, Metaxas D et al (2019) Self-attention generative adversarial networks. In: Proceedings of the International Conference on Machine Learning (ICML), pp 7354\u20137363"},{"key":"8002_CR38","unstructured":"Karras T, Aittala M, Hellsten J et al (2020) Training generative adversarial networks with limited data. In: Advances in Neural Information Processing Systems (NeurIPS), pp 12104\u201312114"},{"key":"8002_CR39","unstructured":"Jolicoeur-Martineau A (2018) The relativistic discriminator: a key element missing from standard GAN. In: Proceedings of the International Conference on Learning Representations (ICLR)"},{"key":"8002_CR40","doi-asserted-by":"crossref","unstructured":"Johnson J, Alahi A, Li F (2016) Perceptual losses for real-time style transfer and super-resolution. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 694\u2013711","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"8002_CR41","unstructured":"Miyato T, Kataoka T, Koyama M et al (2018) Spectral normalization for generative adversarial networks. In: Proceedings of the International Conference on Learning Representations (ICLR)"},{"key":"8002_CR42","unstructured":"Che T, Li Y, Jacob AP et al (2016) Mode regularized generative adversarial networks. arXiv preprint arXiv:1612.02136"},{"key":"8002_CR43","unstructured":"Ulyanov D, Vedaldi A, Lempitsky V (2016) Instance normalization: the missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022"},{"key":"8002_CR44","unstructured":"Heusel M, Ramsauer H, Unterthiner T et al (2017) GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In: Advances in Neural Information Processing Systems (NeurIPS), pp 1\u201312"},{"key":"8002_CR45","unstructured":"Karnewar A, Wang O (2019) MSG-GAN: multi-scale gradient GAN for stable image synthesis. arXiv preprint arXiv:1903.06048v3"},{"key":"8002_CR46","unstructured":"Qin C, Wu Y, Springenberg JT et al (2020) Training generative adversarial networks by solving ordinary differential equations. In: Advances in Neural Information Processing Systems (NeurIPS), pp 5599\u20135609"},{"key":"8002_CR47","unstructured":"Lee K, Chang H, Jiang L et al (2021) VitGAN: training gans with vision transformers. arXiv preprint arXiv:2107.04589"},{"key":"8002_CR48","unstructured":"Mescheder L, Geiger A, Nowozin S (2018) Which training methods for GANs do actually converge? In: Proceedings of the International Conference on Machine Learning (ICML), pp 3481\u20133490"},{"key":"8002_CR49","unstructured":"Thanh-Tung H, Venkatesh S, Tran T (2019) Improving generalization and stability of generative adversarial networks. In: Proceedings of the International Conference on Learning Representations (ICLR)"},{"key":"8002_CR50","doi-asserted-by":"crossref","unstructured":"Choi Y, Uh Y, Yoo J et al (2020) StarGAN v2: diverse image synthesis for multiple domains. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 8188\u20138197","DOI":"10.1109\/CVPR42600.2020.00821"},{"key":"8002_CR51","unstructured":"Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-08002-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-08002-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-08002-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T19:18:19Z","timestamp":1677611899000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-08002-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,17]]},"references-count":51,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["8002"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-08002-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,17]]},"assertion":[{"value":"22 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and\/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled, generative adversarial networks with adaptive learning strategy for noise-to-image synthesis.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}