{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T09:38:16Z","timestamp":1771234696730,"version":"3.50.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T00:00:00Z","timestamp":1659398400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T00:00:00Z","timestamp":1659398400000},"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":["J Sign Process Syst"],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1007\/s11265-022-01796-x","type":"journal-article","created":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T11:18:11Z","timestamp":1659439091000},"page":"13-24","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Semi-Supervised Few-Shot Learning Via Dependency Maximization and Instance Discriminant Analysis"],"prefix":"10.1007","volume":"95","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6836-8288","authenticated-orcid":false,"given":"Zejiang","family":"Hou","sequence":"first","affiliation":[]},{"given":"Sun-Yuan","family":"Kung","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,2]]},"reference":[{"key":"1796_CR1","unstructured":"Chen, W.-Y., Liu, Y.-C., Kira, Z., Wang, Y.-C.\u00a0F., & Huang, J.-B. (2019). A closer look at few-shot classification. ICLR."},{"key":"1796_CR2","doi-asserted-by":"crossref","unstructured":"Raina, R., Battle, A., Lee, H., Packer, B., & Ng, A.\u00a0Y. (2007). Self-taught learning: transfer learning from unlabeled data. ICML.","DOI":"10.1145\/1273496.1273592"},{"key":"1796_CR3","unstructured":"Antoniou, A., Edwards, H., & Storkey, A. (2018). How to train your maml. arXiv preprint arXiv:1810.09502"},{"key":"1796_CR4","unstructured":"Finn, C., Abbeel, P., & Levine, S. (2017). Model-agnostic meta-learning for fast adaptation of deep networks. ICML."},{"key":"1796_CR5","unstructured":"Rusu, A.\u00a0A., Rao, D., Sygnowski, J., Vinyals, O., Pascanu, R., Osindero, S., & Hadsell, R. (2019). Meta-learning with latent embedding optimization. arXiv preprint arXiv:1807.05960"},{"key":"1796_CR6","doi-asserted-by":"crossref","unstructured":"Sun, Q., Liu, Y., Chua, T.-S., & Schiele, B. (2019). Meta-transfer learning for few-shot learning. CVPR.","DOI":"10.1109\/CVPR.2019.00049"},{"key":"1796_CR7","unstructured":"Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et\u00a0al. (2016). Matching networks for one shot learning. NeurIPS."},{"key":"1796_CR8","unstructured":"Snell, J., Swersky, K., & Zemel, R. (2017). Prototypical networks for few-shot learning. NeurIPS."},{"key":"1796_CR9","doi-asserted-by":"crossref","unstructured":"Ye, H.-J., Hu, H., Zhan, D.-C., & Sha, F. (2020). Few-shot learning via embedding adaptation with set-to-set functions. CVPR.","DOI":"10.1109\/CVPR42600.2020.00883"},{"key":"1796_CR10","unstructured":"Hou, R., Chang, H., Bingpeng, M., Shan, S., & Chen, X. (2019). Cross attention network for few-shot classification. NeurIPS."},{"key":"1796_CR11","doi-asserted-by":"crossref","unstructured":"Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.\u00a0H., & Hospedales, T.\u00a0M. (2018). Learning to compare: Relation network for few-shot learning. CVPR.","DOI":"10.1109\/CVPR.2018.00131"},{"key":"1796_CR12","doi-asserted-by":"crossref","unstructured":"Bateni, P., Goyal, R., Masrani, V., Wood, F., & Sigal, L. (2020). Improved few-shot visual classification. CVPR.","DOI":"10.1109\/CVPR42600.2020.01450"},{"key":"1796_CR13","doi-asserted-by":"crossref","unstructured":"Zhang, C., Cai, Y., Lin, G., & Shen, C. (2020). Deepemd: Few-shot image classification with differentiable earth mover\u2019s distance and structured classifiers. CVPR.","DOI":"10.1109\/CVPR42600.2020.01222"},{"key":"1796_CR14","doi-asserted-by":"crossref","unstructured":"Simon, C., Koniusz, P., Nock, R., & Harandi, M. (2020). Adaptive subspaces for few-shot learning. CVPR.","DOI":"10.1109\/CVPR42600.2020.00419"},{"key":"1796_CR15","unstructured":"Gao, H., Shou, Z., Zareian, A., Zhang, H., & Chang, S.-F. (2018). Low-shot learning via covariance-preserving adversarial augmentation networks. NeurIPS."},{"key":"1796_CR16","doi-asserted-by":"crossref","unstructured":"Li, K., Zhang, Y., Li, K., & Fu, Y. (2020). Adversarial feature hallucination networks for few-shot learning. CVPR.","DOI":"10.1109\/CVPR42600.2020.01348"},{"key":"1796_CR17","unstructured":"Zhang, R., Che, T., Ghahramani, Z., Bengio, Y., & Song, Y. (2018). Metagan: An adversarial approach to few-shot learning. NeurIPS."},{"key":"1796_CR18","unstructured":"Liu, Y., Lee, J., Park, M., Kim, S., Yang, E., Hwang, S.\u00a0J., & Yang, Y. (2018). Learning to propagate labels: Transductive propagation network for few-shot learning. arXiv preprint arXiv:1805.10002"},{"key":"1796_CR19","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez, P., Laradji, I., Drouin, A., & Lacoste, A. (2020). Embedding propagation: Smoother manifold for few-shot classification. ECCV.","DOI":"10.1007\/978-3-030-58574-7_8"},{"key":"1796_CR20","unstructured":"Hu, S.\u00a0X., Moreno, P.\u00a0G., Xiao, Y., Shen, X., Obozinski, G., Lawrence, N.\u00a0D., & Damianou, A. (2020). Empirical bayes transductive meta-learning with synthetic gradients. ICLR."},{"key":"1796_CR21","unstructured":"Dhillon, G.\u00a0S., Chaudhari, P., Ravichandran, A., & Soatto, S. (2020). A baseline for few-shot image classification. ICLR."},{"key":"1796_CR22","unstructured":"Boudiaf, M., Masud, Z.\u00a0I., Rony, J., Dolz, J., Piantanida, P., & Ayed, I.\u00a0B. (2020). Transductive information maximization for few-shot learning. NeurIPS."},{"key":"1796_CR23","unstructured":"Li, X., Sun, Q., Liu, Y., Zhou, Q., Zheng, S., Chua, T.-S., & Schiele, B. (2019). Learning to self-train for semi-supervised few-shot classification. NeurIPS."},{"key":"1796_CR24","unstructured":"Ren, M., Triantafillou, E., Ravi, S., Snell, J., Swersky, K., Tenenbaum, J.\u00a0B., Larochelle, H., & Zemel, R.\u00a0S. (2018). Meta-learning for semi-supervised few-shot classification. ICLR."},{"key":"1796_CR25","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xu, C., Liu, C., Zhang, L., & Fu, Y. (2020). Instance credibility inference for few-shot learning. CVPR.","DOI":"10.1109\/CVPR42600.2020.01285"},{"key":"1796_CR26","doi-asserted-by":"crossref","unstructured":"Baker, C. R. (1973). Joint measures and cross-covariance operators. Transactions of the American Mathematical Society.","DOI":"10.1090\/S0002-9947-1973-0336795-3"},{"key":"1796_CR27","doi-asserted-by":"crossref","unstructured":"Gretton, A., Bousquet, O., Smola, A., & Sch\u00f6lkopf, B. (2005). Measuring statistical dependence with hilbert-schmidt norms. ALT.","DOI":"10.1007\/11564089_7"},{"key":"1796_CR28","unstructured":"Ziko, I., Dolz, J., Granger, E., & Ayed, I.\u00a0B. (2020). Laplacian regularized few-shot learning. ICML."},{"key":"1796_CR29","doi-asserted-by":"crossref","unstructured":"Liu, J., Song, L., & Qin, Y. (2020a). Prototype rectification for few-shot learning. ECCV.","DOI":"10.1007\/978-3-030-58452-8_43"},{"key":"1796_CR30","doi-asserted-by":"crossref","unstructured":"Lichtenstein, M., Sattigeri, P., Feris, R., Giryes, R., & Karlinsky, L. (2020). Tafssl: Task-adaptive feature sub-space learning for few-shot classification. ECCV.","DOI":"10.1007\/978-3-030-58571-6_31"},{"key":"1796_CR31","doi-asserted-by":"crossref","unstructured":"Lee, K., Maji, S., Ravichandran, A., & Soatto, S. (2019). Meta-learning with differentiable convex optimization. CVPR.","DOI":"10.1109\/CVPR.2019.01091"},{"key":"1796_CR32","volume-title":"Balasubramanian","author":"P Mangla","year":"2020","unstructured":"Mangla, P., Kumari, N., Sinha, A., Singh, M., & Krishnamurthy, B. (2020). Balasubramanian (Vol. N). Manifold mixup for few-shot learning. WACV: Charting the right manifold."},{"key":"1796_CR33","unstructured":"Ravi, S., & Larochelle, H. (2017). Optimization as a model for few-shot learning. ICLR."},{"key":"1796_CR34","unstructured":"Wah, C., Branson, S., Welinder, P., Perona, P., & Belongie, S. (2011). The caltech-ucsd birds-200-2011 dataset. Computation and Neural Systems Technical Report."},{"key":"1796_CR35","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., & Komodakis, N. (2016). Wide residual networks. BMVC.","DOI":"10.5244\/C.30.87"},{"key":"1796_CR36","doi-asserted-by":"crossref","unstructured":"Zhang, C., Lin, G., Liu, F., Yao, R., & Shen, C. (2019b). Canet: Class-agnostic segmentation networks with iterative refinement and attentive few-shot learning. CVPR.","DOI":"10.1109\/CVPR.2019.00536"},{"key":"1796_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, C., Lin, G., Liu, F., Guo, J., Wu, Q., & Yao, R. (2019a). Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation. CVPR.","DOI":"10.1109\/ICCV.2019.00968"},{"key":"1796_CR38","doi-asserted-by":"crossref","unstructured":"Liu, W., Zhang, C., Lin, G., & Liu, F. (2020b). Crnet: Cross-reference networks for few-shot segmentation. CVPR.","DOI":"10.1109\/CVPR42600.2020.00422"},{"key":"1796_CR39","doi-asserted-by":"crossref","unstructured":"Gairola, S., Hemani, M., Chopra, A., & Krishnamurthy, B. (2020). Simpropnet: Improved similarity propagation for few-shot image segmentation. arXiv preprint arXiv:2004.15014","DOI":"10.24963\/ijcai.2020\/80"},{"key":"1796_CR40","doi-asserted-by":"crossref","unstructured":"Yang, Y., Meng, F., Li, H., Wu, Q., Xu, X., & Chen, S. (2020b). A new local transformation module for few-shot segmentation. ICMM.","DOI":"10.1007\/978-3-030-37734-2_7"},{"key":"1796_CR41","doi-asserted-by":"crossref","unstructured":"Yang, B., Liu, C., Li, B., Jiao, J., & Ye, Q. (2020a). Prototype mixture models for few-shot semantic segmentation. ECCV.","DOI":"10.1007\/978-3-030-58598-3_45"},{"key":"1796_CR42","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, X., Zhang, S., & He, X. (2020c). Part-aware prototype network for few-shot semantic segmentation. ECCV.","DOI":"10.1007\/978-3-030-58545-7_9"},{"key":"1796_CR43","doi-asserted-by":"crossref","unstructured":"Everingham, M., Van\u00a0Gool, L., Williams, C.\u00a0K., Winn, J., & Zisserman, A. (2010). The pascal visual object classes (voc) challenge. IJCV.","DOI":"10.1007\/s11263-009-0275-4"},{"key":"1796_CR44","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017). Pyramid scene parsing network. CVPR.","DOI":"10.1109\/CVPR.2017.660"},{"key":"1796_CR45","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/0024-3795(94)90473-1","volume":"210","author":"JK Merikoski","year":"1994","unstructured":"Merikoski, J. K., Sarria, H., & Tarazaga, P. (1994). Bounds for singular values using traces. Linear Algebra and its Applications, 210, 227\u2013254.","journal-title":"Linear Algebra and its Applications"},{"key":"1796_CR46","unstructured":"Von Neumann, J. (1937). \u201cSome Matrix-Inequalities and Metrization of Matrix-Space. Rev: Tomsk. Univ.\u00a0"},{"key":"1796_CR47","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139020411","volume-title":"Matrix analysis","author":"RA Horn","year":"2012","unstructured":"Horn, R. A., & Johnson, C. R. (2012). Matrix analysis. Cambridge University Press."}],"container-title":["Journal of Signal Processing Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11265-022-01796-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11265-022-01796-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11265-022-01796-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T19:28:26Z","timestamp":1677612506000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11265-022-01796-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,2]]},"references-count":47,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["1796"],"URL":"https:\/\/doi.org\/10.1007\/s11265-022-01796-x","relation":{},"ISSN":["1939-8018","1939-8115"],"issn-type":[{"value":"1939-8018","type":"print"},{"value":"1939-8115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,2]]},"assertion":[{"value":"1 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 June 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 July 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 August 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}