{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T02:51:49Z","timestamp":1767495109244,"version":"3.37.3"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T00:00:00Z","timestamp":1682294400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T00:00:00Z","timestamp":1682294400000},"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 Comput & Applic"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s00521-023-08530-z","type":"journal-article","created":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T14:03:20Z","timestamp":1682345000000},"page":"16593-16605","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["InvolutionGAN: lightweight GAN with involution for unsupervised image-to-image translation"],"prefix":"10.1007","volume":"35","author":[{"given":"Haipeng","family":"Deng","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2284-7806","authenticated-orcid":false,"given":"Qiuxia","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Han","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Xiaowei","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Zhiyong","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,24]]},"reference":[{"key":"8530_CR1","unstructured":"Aguinaldo A, Chiang PY, Gain A, et\u00a0al (2019) Compressing gans using knowledge distillation. arXiv preprint arXiv:1902.00159"},{"key":"8530_CR2","unstructured":"Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In: International conference on machine learning, PMLR, pp 214\u2013223"},{"key":"8530_CR3","doi-asserted-by":"crossref","unstructured":"Bharti V, Biswas B, Shukla KK (2021) Emocgan: a novel evolutionary multiobjective cyclic generative adversarial network and its application to unpaired image translation. Neural Comput Appl, pp 1\u201315","DOI":"10.1007\/s00521-021-05975-y"},{"key":"8530_CR4","unstructured":"Bi\u0144kowski M, Sutherland DJ, Arbel M, et\u00a0al (2018) Demystifying mmd gans. arXiv preprint arXiv:1801.01401"},{"key":"8530_CR5","doi-asserted-by":"crossref","unstructured":"Cao Y, Zhou Z, Zhang W, et\u00a0al (2017) Unsupervised diverse colorization via generative adversarial networks. In: Joint European conference on machine learning and knowledge discovery in databases, Springer, pp 151\u2013166","DOI":"10.1007\/978-3-319-71249-9_10"},{"key":"8530_CR6","doi-asserted-by":"crossref","unstructured":"Chen R, Huang W, Huang B, et\u00a0al (2020) Reusing discriminators for encoding: Towards unsupervised image-to-image translation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8168\u20138177","DOI":"10.1109\/CVPR42600.2020.00819"},{"key":"8530_CR7","unstructured":"Chen X, Duan Y, Houthooft R, et\u00a0al (2016) Infogan: interpretable representation learning by information maximizing generative adversarial nets. In: Proceedings of the 30th international conference on neural information processing systems, pp 2180\u20132188"},{"key":"8530_CR8","doi-asserted-by":"crossref","unstructured":"Choi Y, Choi M, Kim M, et\u00a0al (2018) Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8789\u20138797","DOI":"10.1109\/CVPR.2018.00916"},{"key":"8530_CR9","doi-asserted-by":"crossref","unstructured":"Choi Y, Uh Y, Yoo J, et\u00a0al (2020) Stargan v2: diverse image synthesis for multiple domains. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8188\u20138197","DOI":"10.1109\/CVPR42600.2020.00821"},{"key":"8530_CR10","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1109\/TMM.2020.2975961","volume":"23","author":"H Emami","year":"2020","unstructured":"Emami H, Aliabadi MM, Dong M et al (2020) Spa-gan: spatial attention gan for image-to-image translation. IEEE Trans Multimed 23:391\u2013401","journal-title":"IEEE Trans Multimed"},{"key":"8530_CR11","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, et\u00a0al (2014) Generative adversarial nets. Adv Neural Inf Process Syst 27"},{"key":"8530_CR12","unstructured":"Gulrajani I, Ahmed F, Arjovsky M, et\u00a0al (2017) Improved training of wasserstein gans. arXiv preprint arXiv:1704.00028"},{"key":"8530_CR13","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, et\u00a0al (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":"8530_CR14","unstructured":"Heusel M, Ramsauer H, Unterthiner T, et\u00a0al (2017) Gans trained by a two time-scale update rule converge to a local nash equilibrium. Adv Neural Inf Process Syst 30"},{"key":"8530_CR15","doi-asserted-by":"crossref","unstructured":"Huang X, Liu MY, Belongie S, et\u00a0al (2018) Multimodal unsupervised image-to-image translation. In: Proceedings of the European conference on computer vision (ECCV), pp 172\u2013189","DOI":"10.1007\/978-3-030-01219-9_11"},{"issue":"4","key":"8530_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2897824.2925974","volume":"35","author":"S Iizuka","year":"2016","unstructured":"Iizuka S, Simo-Serra E, Ishikawa H (2016) Let there be color! joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification. ACM Trans Graph 35(4):1\u201311","journal-title":"ACM Trans Graph"},{"key":"8530_CR17","doi-asserted-by":"crossref","unstructured":"Isola P, Zhu JY, Zhou T, et\u00a0al (2017) Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1125\u20131134","DOI":"10.1109\/CVPR.2017.632"},{"key":"8530_CR18","doi-asserted-by":"crossref","unstructured":"Jeong S, Kim Y, Lee E, et\u00a0al (2021) Memory-guided unsupervised image-to-image translation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 6558\u20136567","DOI":"10.1109\/CVPR46437.2021.00649"},{"key":"8530_CR19","doi-asserted-by":"crossref","unstructured":"Jo Y, Yang S, Kim SJ (2020) Investigating loss functions for extreme super-resolution. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops, pp 424\u2013425","DOI":"10.1109\/CVPRW50498.2020.00220"},{"key":"8530_CR20","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":"8530_CR21","doi-asserted-by":"crossref","unstructured":"Kang T, Lee KH (2020) Unsupervised image-to-image translation with self-attention networks. In: 2020 IEEE international conference on big data and smart computing (BigComp), IEEE, pp 102\u2013108","DOI":"10.1109\/BigComp48618.2020.00-92"},{"key":"8530_CR22","unstructured":"Kim J, Kim M, Kang H, et\u00a0al (2019) U-gat-it: unsupervised generative attentional networks with adaptive layer-instance normalization for image-to-image translation. arXiv preprint arXiv:1907.10830"},{"key":"8530_CR23","unstructured":"Kim T, Cha M, Kim H, et\u00a0al (2017) Learning to discover cross-domain relations with generative adversarial networks. In: International conference on machine learning, PMLR, pp 1857\u20131865"},{"key":"8530_CR24","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"8530_CR25","doi-asserted-by":"crossref","unstructured":"Ledig C, Theis L, Husz\u00e1r F, et\u00a0al (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4681\u20134690","DOI":"10.1109\/CVPR.2017.19"},{"key":"8530_CR26","doi-asserted-by":"crossref","unstructured":"Lee HY, Tseng HY, Huang JB, et\u00a0al (2018) Diverse image-to-image translation via disentangled representations. In: Proceedings of the European conference on computer vision (ECCV), pp 35\u201351","DOI":"10.1007\/978-3-030-01246-5_3"},{"issue":"10","key":"8530_CR27","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1007\/s11263-019-01284-z","volume":"128","author":"HY Lee","year":"2020","unstructured":"Lee HY, Tseng HY, Mao Q et al (2020) Drit++: diverse image-to-image translation via disentangled representations. Int J Comput Vis 128(10):2402\u20132417","journal-title":"Int J Comput Vis"},{"key":"8530_CR28","doi-asserted-by":"crossref","unstructured":"Li D, Hu J, Wang C, et\u00a0al (2021) Involution: inverting the inherence of convolution for visual recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12321\u201312330","DOI":"10.1109\/CVPR46437.2021.01214"},{"key":"8530_CR29","doi-asserted-by":"crossref","unstructured":"Li M, Lin J, Ding Y, et\u00a0al (2020) Gan compression: Efficient architectures for interactive conditional gans. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5284\u20135294","DOI":"10.1109\/CVPR42600.2020.00533"},{"key":"8530_CR30","unstructured":"Liu MY, Breuel T, Kautz J (2017) Unsupervised image-to-image translation networks. In: Advances in neural information processing systems, pp 700\u2013708"},{"issue":"2","key":"8530_CR31","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1007\/s11263-021-01557-6","volume":"130","author":"Q Mao","year":"2022","unstructured":"Mao Q, Tseng HY, Lee HY et al (2022) Continuous and diverse image-to-image translation via signed attribute vectors. Int J Comput Vis 130(2):517\u2013549","journal-title":"Int J Comput Vis"},{"key":"8530_CR32","doi-asserted-by":"crossref","unstructured":"Mao X, Li Q, Xie H, et\u00a0al (2017) Least squares generative adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp 2794\u20132802","DOI":"10.1109\/ICCV.2017.304"},{"key":"8530_CR33","unstructured":"Mejjati YA, Richardt C, Tompkin J, et\u00a0al (2018) Unsupervised attention-guided image to image translation. arXiv preprint arXiv:1806.02311"},{"key":"8530_CR34","unstructured":"Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784"},{"key":"8530_CR35","doi-asserted-by":"crossref","unstructured":"Peng X, Peng S, Hu Q, et\u00a0al (2022) Contour-enhanced cyclegan framework for style transfer from scenery photos to Chinese landscape paintings. Neural Comput Appl, pp 1\u201322","DOI":"10.1007\/s00521-022-07432-w"},{"issue":"5","key":"8530_CR36","doi-asserted-by":"publisher","first-page":"1118","DOI":"10.1007\/s11263-019-01265-2","volume":"128","author":"GJ Qi","year":"2020","unstructured":"Qi GJ (2020) Loss-sensitive generative adversarial networks on lipschitz densities. Int J Comput Vis 128(5):1118\u20131140","journal-title":"Int J Comput Vis"},{"key":"8530_CR37","doi-asserted-by":"crossref","unstructured":"Qu X, Wang X, Wang Z, et\u00a0al (2018) Perceptual-dualgan: perceptual losses for image to image translation with generative adversarial nets. In: 2018 international joint conference on neural networks (IJCNN), IEEE, pp 1\u20138","DOI":"10.1109\/IJCNN.2018.8489108"},{"key":"8530_CR38","unstructured":"Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434"},{"key":"8530_CR39","doi-asserted-by":"crossref","unstructured":"Shi W, Caballero J, Husz\u00e1r F, et\u00a0al (2016) Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1874\u20131883","DOI":"10.1109\/CVPR.2016.207"},{"key":"8530_CR40","doi-asserted-by":"crossref","unstructured":"Tang H, Xu D, Sebe N, et\u00a0al (2019) Multi-channel attention selection gan with cascaded semantic guidance for cross-view image translation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2417\u20132426","DOI":"10.1109\/CVPR.2019.00252"},{"key":"8530_CR41","unstructured":"Ulyanov D, Vedaldi A, Lempitsky V (2016) Instance normalization: the missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022"},{"key":"8530_CR42","doi-asserted-by":"crossref","unstructured":"Wang TC, Liu MY, Zhu JY, et\u00a0al (2018) High-resolution image synthesis and semantic manipulation with conditional gans. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8798\u20138807","DOI":"10.1109\/CVPR.2018.00917"},{"issue":"9","key":"8530_CR43","doi-asserted-by":"publisher","first-page":"7209","DOI":"10.1007\/s00521-021-06841-7","volume":"34","author":"J Xiao","year":"2022","unstructured":"Xiao J, Zhang S, Yao Y et al (2022) Generative adversarial network with hybrid attention and compromised normalization for multi-scene image conversion. Neural Comput Appl 34(9):7209\u20137225","journal-title":"Neural Comput Appl"},{"issue":"11","key":"8530_CR44","doi-asserted-by":"publisher","first-page":"7333","DOI":"10.1007\/s00521-019-04253-2","volume":"32","author":"S Xu","year":"2020","unstructured":"Xu S, Zhu Q, Wang J (2020) Generative image completion with image-to-image translation. Neural Comput Appl 32(11):7333\u20137345","journal-title":"Neural Comput Appl"},{"key":"8530_CR45","doi-asserted-by":"crossref","unstructured":"Yadav NK, Singh SK, Dubey SR (2022) Csa-gan: cyclic synthesized attention guided generative adversarial network for face synthesis. Appl Intell, pp 1\u201320","DOI":"10.1007\/s10489-021-03064-0"},{"key":"8530_CR46","doi-asserted-by":"crossref","unstructured":"Yi Z, Zhang H, Tan P, et\u00a0al (2017) Dualgan: Unsupervised dual learning for image-to-image translation. In: Proceedings of the IEEE international conference on computer vision, pp 2849\u20132857","DOI":"10.1109\/ICCV.2017.310"},{"key":"8530_CR47","doi-asserted-by":"crossref","unstructured":"Zhang R, Isola P, Efros AA (2016) Colorful image colorization. In: European conference on computer vision, Springer, pp 649\u2013666","DOI":"10.1007\/978-3-319-46487-9_40"},{"key":"8530_CR48","doi-asserted-by":"crossref","unstructured":"Zhang Y, Yu L, Sun B, et\u00a0al (2022) Eng-face: cross-domain heterogeneous face synthesis with enhanced asymmetric cyclegan. Appl Intell pp 1\u201313","DOI":"10.1007\/s10489-022-03302-z"},{"key":"8530_CR49","doi-asserted-by":"crossref","unstructured":"Zhou D, Zhang H, Li Q, et\u00a0al (2022) Coutfitgan: learning to synthesize compatible outfits supervised by silhouette masks and fashion styles. IEEE Trans Multimed","DOI":"10.1109\/TMM.2022.3185894"},{"key":"8530_CR50","doi-asserted-by":"crossref","unstructured":"Zhu JY, Park T, Isola P, et\u00a0al (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp 2223\u20132232","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08530-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08530-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08530-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T20:51:20Z","timestamp":1689195080000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08530-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,24]]},"references-count":50,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["8530"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08530-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2023,4,24]]},"assertion":[{"value":"17 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration"}},{"value":"Haipeng Deng, Qiuxia Wu, Han Huang, Xiaowei Yang and Zhiyong Wang declare that no conflict of interest could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}