{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T06:21:37Z","timestamp":1775542897387,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T00:00:00Z","timestamp":1658361600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T00:00:00Z","timestamp":1658361600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2020YFA0907800"],"award-info":[{"award-number":["2020YFA0907800"]}]},{"name":"National Key Research and Development Program of China","award":["2018YFC0807105"],"award-info":[{"award-number":["2018YFC0807105"]}]},{"name":"Science and Technology Committee of Shanghai Municipality","award":["17DZ1101003"],"award-info":[{"award-number":["17DZ1101003"]}]},{"name":"Science and Technology Committee of Shanghai Municipality","award":["18511106602"],"award-info":[{"award-number":["18511106602"]}]},{"name":"Science and Technology Committee of Shanghai Municipality","award":["18DZ2252300"],"award-info":[{"award-number":["18DZ2252300"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s00371-022-02566-3","type":"journal-article","created":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T18:02:49Z","timestamp":1658426569000},"page":"4015-4028","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Few-shot image generation based on contrastive meta-learning generative adversarial network"],"prefix":"10.1007","volume":"39","author":[{"given":"Aniwat","family":"Phaphuangwittayakul","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8390-3229","authenticated-orcid":false,"given":"Fangli","family":"Ying","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Liting","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Nopasit","family":"Chakpitak","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,21]]},"reference":[{"key":"2566_CR1","unstructured":"Van Oord, A., Kalchbrenner, N., Kavukcuoglu, K.: Pixel recurrent neural networks. In: International Conference on Machine Learning, pp. 1747\u20131756 (2016)"},{"key":"2566_CR2","unstructured":"Rezende, D.J., Mohamed, S.: Variational inference with normalizing flows. In: International Conference on Machine Learning (2015)"},{"key":"2566_CR3","first-page":"2672","volume":"27","author":"I Goodfellow","year":"2014","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. Adv. Neural Inf. Process. Syst. 27, 2672\u20132680 (2014)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"2566_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00371-021-02218-y","volume":"37","author":"H Li","year":"2021","unstructured":"Li, H., Zhong, Z., Guan, W., Du, C., Yang, Y., Wei, Y., Ye, C.: Generative character inpainting guided by structural information. Vis. Comput. 37, 1\u201312 (2021)","journal-title":"Vis. Comput."},{"key":"2566_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00371-021-02236-w","volume":"37","author":"L Li","year":"2021","unstructured":"Li, L., Tang, J., Ye, Z., Sheng, B., Mao, L., Ma, L.: Unsupervised face super-resolution via gradient enhancement and semantic guidance. Vis. Comput. 37, 1\u201313 (2021)","journal-title":"Vis. Comput."},{"key":"2566_CR6","unstructured":"Kingma, D.P., Welling, M.: Auto-Encoding Variational Bayes. CoRR arXiv:1312.6114 (2014)"},{"key":"2566_CR7","unstructured":"Bartunov, S., Vetrov, D.: Few-shot generative modelling with generative matching networks. In: International Conference on Artificial Intelligence and Statistics, pp. 670\u2013678 (2018)"},{"key":"2566_CR8","unstructured":"Clou\u00e2tre, L., Demers, M.: FIGR: few-shot image generation with reptile. CoRR (2019)"},{"key":"2566_CR9","unstructured":"Liang, W., Liu, Z., Liu, C.: DAWSON: a domain adaptive few shot generation framework. CoRR arXiv:2001.00576 (2020)"},{"key":"2566_CR10","doi-asserted-by":"publisher","first-page":"2205","DOI":"10.1109\/TMM.2021.3077729","volume":"24","author":"A Phaphuangwittayakul","year":"2021","unstructured":"Phaphuangwittayakul, A., Guo, Y., Ying, F.: Fast adaptive meta-learning for few-shot image generation. IEEE Trans. Multimed. 24, 2205\u20132217 (2021)","journal-title":"IEEE Trans. Multimed."},{"issue":"1","key":"2566_CR11","doi-asserted-by":"publisher","first-page":"2","DOI":"10.3390\/technologies9010002","volume":"9","author":"A Jaiswal","year":"2021","unstructured":"Jaiswal, A., Babu, A.R., Zadeh, M.Z., Banerjee, D., Makedon, F.: A survey on contrastive self-supervised learning. Technologies 9(1), 2 (2021)","journal-title":"Technologies"},{"key":"2566_CR12","unstructured":"Wang, Y., Wu, X.-M., Li, Q., Gu, J., Xiang, W., Zhang, L., Li, V.O.K.: Large margin few-shot learning. CoRR arXiv:1807.02872 (2018)"},{"key":"2566_CR13","doi-asserted-by":"crossref","unstructured":"Xiao, C., Madapana, N., Wachs, J.: One-shot image recognition using prototypical encoders with reduced hubness. In: Proceedings of IEEE\/CVF Winter Conference on Applied Computing and Vision, pp. 2252\u20132261 (2021)","DOI":"10.1109\/WACV48630.2021.00230"},{"key":"2566_CR14","unstructured":"Andrychowicz, M., Denil, M., Colmenarejo, S.G., Hoffman, M.W., Pfau, D., Schaul, T., de Freitas, N.: Learning to learn by gradient descent by gradient descent. CoRR arXiv:1606.04474 (2016)"},{"key":"2566_CR15","unstructured":"Munkhdalai, T., Yu, H.: Meta networks. In: International Conference on Machine Learning, pp. 2554\u20132563 (2017)"},{"key":"2566_CR16","doi-asserted-by":"crossref","unstructured":"Gidaris, S., Komodakis, N.: Dynamic few-shot visual learning without forgetting. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 4367\u20134375 (2018)","DOI":"10.1109\/CVPR.2018.00459"},{"key":"2566_CR17","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, pp. 1126\u20131135 (2017)"},{"key":"2566_CR18","unstructured":"Nichol, A., Achiam, J., Schulman, J.: On First-Order Meta-Learning Algorithms. CoRR arXiv:1803.02999 (2018)"},{"key":"2566_CR19","doi-asserted-by":"crossref","unstructured":"Jamal, M.A., Qi, G.-J.: Task agnostic meta-learning for few-shot learning. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11719\u201311727 (2019)","DOI":"10.1109\/CVPR.2019.01199"},{"key":"2566_CR20","unstructured":"Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning. In: International Conference on Learning Representations. OpenReview.net (2017)"},{"key":"2566_CR21","unstructured":"Lake, B., Salakhutdinov, R., Gross, J., Tenenbaum, J.: One shot learning of simple visual concepts. In: Proceedings of Annual Meeting of the Cognitive Science Society, vol. 33, No. 33 (2011)"},{"key":"2566_CR22","unstructured":"Rezende, D.J., Mohamed, S., Danihelka, I., Gregor, K., Wierstra, D.: One-shot generalization in deep generative models. arXiv preprint arXiv:1603.05106 (2016)"},{"key":"2566_CR23","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)"},{"key":"2566_CR24","doi-asserted-by":"crossref","unstructured":"Antoniou, A., Storkey, A.J., Edwards, H.: Data augmentation generative adversarial networks. CoRR arXiv:1711.04340 (2017)","DOI":"10.1007\/978-3-030-01424-7_58"},{"key":"2566_CR25","doi-asserted-by":"crossref","unstructured":"Hong, Y., Niu, L., Zhang, J., Zhang, L.: MatchingGAN: matching-based few-shot image generation. In: 2020 IEEE International Conference on Multimedia Expo, pp. 1\u20136 (2020)","DOI":"10.1109\/ICME46284.2020.9102917"},{"key":"2566_CR26","doi-asserted-by":"crossref","unstructured":"Hong, Y., Niu, L., Zhang, J., Zhao, W., Fu, C., Zhang, L.: F2GAN: fusing-and-filling GAN for few-shot image generation. In: Proceedings of 28th ACM International Conference on Multimedia, pp. 2535\u20132543 (2020)","DOI":"10.1145\/3394171.3413561"},{"key":"2566_CR27","unstructured":"van den Oord, A., Li, Y., Vinyals, O.: Representation Learning with Contrastive Predictive Coding. CoRR arXiv:1807.03748 (2018)"},{"key":"2566_CR28","unstructured":"Li, J., Zhou, P., Xiong, C., Hoi, S.C.H.: Prototypical contrastive learning of unsupervised representations. In: International Conference on Learning Representations. OpenReview.net (2021)"},{"key":"2566_CR29","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607 (2020)"},{"key":"2566_CR30","doi-asserted-by":"crossref","unstructured":"Tian, Y., Krishnan, D., Isola, P.: Contrastive multiview coding. In: European Conference on Computer Vision, vol. 12356, pp. 776\u2013794. Springer (2020)","DOI":"10.1007\/978-3-030-58621-8_45"},{"key":"2566_CR31","doi-asserted-by":"crossref","unstructured":"Wang, J., Wang, Y., Liu, S., Li, A.: Few-shot fine-grained action recognition via bidirectional attention and contrastive meta-learning. CoRR arXiv:2108.06647 (2021)","DOI":"10.1145\/3474085.3475216"},{"key":"2566_CR32","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2015)"},{"key":"2566_CR33","unstructured":"LeCun, Y., Cortes, C.: MNIST handwritten digit database. AT&T Labs. Available http:\/\/yann.lecun.com\/exdb\/mnist (2010)"},{"key":"2566_CR34","doi-asserted-by":"crossref","unstructured":"Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A.: Vggface2: a dataset for recognising faces across pose and age. In: 2018 13th IEEE International Conference on Automatic Face and Gesture Recognitions (FG 2018), pp. 67\u201374. IEEE (2018)","DOI":"10.1109\/FG.2018.00020"},{"key":"2566_CR35","unstructured":"Jolicoeur-Martineau, A.: The relativistic discriminator: a key element missing from standard GaN. In: International Conference on Learning Representations (2019)"},{"key":"2566_CR36","doi-asserted-by":"crossref","unstructured":"Mao, Q., Lee, H.Y., Tseng, H.Y., Ma, S., Yang, M.H.: Mode seeking generative adversarial networks for diverse image synthesis. In: Proceedings of IEEE Computer Society Conference Computer Vision and Pattern Recognitions, pp. 1429\u20131437 (2019)","DOI":"10.1109\/CVPR.2019.00152"},{"key":"2566_CR37","first-page":"6626","volume":"30","author":"M Heusel","year":"2017","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Adv. Neural Inf. Process. Syst. 30, 6626\u20136637 (2017)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"2566_CR38","doi-asserted-by":"crossref","unstructured":"Xu, Q., Huang, G., Yuan, Y., Guo, C., Sun, Y., Wu, F., Weinberger, K.Q.: An empirical study on evaluation metrics of generative adversarial networks. CoRR arXiv:1806.07755 (2018)","DOI":"10.1109\/BigData.2018.8622525"},{"key":"2566_CR39","doi-asserted-by":"crossref","unstructured":"Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H.S., Hospedales, T.M.: Learning to compare: relation network for few-shot learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognitions, pp. 1199\u20131208 (2018)","DOI":"10.1109\/CVPR.2018.00131"},{"key":"2566_CR40","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognitions, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2566_CR41","unstructured":"Varghese, D., Tamaddoni-Nezhad, A., Moschoyiannis, S., Fodor, P., Vanthienen, J., Inclezan, D., Nikolov, N.: One-shot rule learning for challenging character recognition. RuleML+ RR, pp. 10\u201327 (2020)"},{"issue":"6266","key":"2566_CR42","doi-asserted-by":"publisher","first-page":"1332","DOI":"10.1126\/science.aab3050","volume":"350","author":"BM Lake","year":"2015","unstructured":"Lake, B.M., Salakhutdinov, R., Tenenbaum, J.B.: Human-level concept learning through probabilistic program induction. Science (80-) 350(6266), 1332\u20131338 (2015)","journal-title":"Science (80-)"},{"key":"2566_CR43","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)"},{"key":"2566_CR44","unstructured":"Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of ICML, vol. 30, No. 1, p. 3 (2013)"},{"key":"2566_CR45","unstructured":"Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. In: International Conference on Learning Representations (ICLR) (2018)"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-022-02566-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-022-02566-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-022-02566-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T15:12:26Z","timestamp":1693581146000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-022-02566-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,21]]},"references-count":45,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["2566"],"URL":"https:\/\/doi.org\/10.1007\/s00371-022-02566-3","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,21]]},"assertion":[{"value":"30 May 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 July 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 no conflict of interest in the submission of this article for publication.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}