{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:42:21Z","timestamp":1764175341295,"version":"3.40.3"},"publisher-location":"Cham","reference-count":68,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031234972"},{"type":"electronic","value":"9783031234989"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-23498-9_5","type":"book-chapter","created":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T19:04:33Z","timestamp":1670958273000},"page":"54-68","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Improving Few-Shot Image Classification with\u00a0Self-supervised Learning"],"prefix":"10.1007","author":[{"given":"Shisheng","family":"Deng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongping","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xitong","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juanjuan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kejiang","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,14]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Ali-Gombe, A., Elyan, E., Savoye, Y., Jayne, C.: Few-shot classifier GAN. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2018)","DOI":"10.1109\/IJCNN.2018.8489387"},{"issue":"4","key":"5_CR2","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1021\/acscentsci.6b00367","volume":"3","author":"H Altae-Tran","year":"2017","unstructured":"Altae-Tran, H., Ramsundar, B., Pappu, A.S., Pande, V.: Low data drug discovery with one-shot learning. ACS Cent. Sci. 3(4), 283\u2013293 (2017)","journal-title":"ACS Cent. Sci."},{"key":"5_CR3","unstructured":"Antoniou, A., Edwards, H., Storkey, A.: How to train your MAML. In: International Conference on Learning Representations (2018)"},{"key":"5_CR4","unstructured":"Bachman, P., Hjelm, R.D., Buchwalter, W.: Learning representations by maximizing mutual information across views. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Bateni, P., Barber, J., van de Meent, J.W., Wood, F.: Enhancing few-shot image classification with unlabelled examples. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2796\u20132805 (2022)","DOI":"10.1109\/WACV51458.2022.00166"},{"key":"5_CR6","unstructured":"Boudiaf, M., Ziko, I., Rony, J., Dolz, J., Piantanida, P., Ben Ayed, I.: Information maximization for few-shot learning. In: Advances in Neural Information Processing Systems, vol. 33, pp. 2445\u20132457 (2020)"},{"key":"5_CR7","unstructured":"Bronskill, J., Gordon, J., Requeima, J., Nowozin, S., Turner, R.: TaskNorm: rethinking batch normalization for meta-learning. In: International Conference on Machine Learning, pp. 1153\u20131164. PMLR (2020)"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Caron, M., Bojanowski, P., Joulin, A., Douze, M.: Deep clustering for unsupervised learning of visual features. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 132\u2013149 (2018)","DOI":"10.1007\/978-3-030-01264-9_9"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Caron, M., Bojanowski, P., Mairal, J., Joulin, A.: Unsupervised pre-training of image features on non-curated data. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2959\u20132968 (2019)","DOI":"10.1109\/ICCV.2019.00305"},{"key":"5_CR10","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. PMLR (2020)"},{"key":"5_CR11","unstructured":"Chen, W.Y., Liu, Y.C., Kira, Z., Wang, Y.C.F., Huang, J.B.: A closer look at few-shot classification. In: International Conference on Learning Representations (2018)"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Chen, Z., Ge, J., Zhan, H., Huang, S., Wang, D.: Pareto self-supervised training for few-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13663\u201313672 (2021)","DOI":"10.1109\/CVPR46437.2021.01345"},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Chen, Z., Maji, S., Learned-Miller, E.: Shot in the dark: few-shot learning with no base-class labels. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2668\u20132677 (2021)","DOI":"10.1109\/CVPRW53098.2021.00300"},{"key":"5_CR14","unstructured":"Co-Reyes, J.D., et al.: Meta-learning language-guided policy learning. In: International Conference on Learning Representations, vol. 3 (2019)"},{"key":"5_CR15","unstructured":"Craig, J.J.: Introduction to Robotics: Mechanics and Control. Pearson Educacion (2005)"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"5_CR17","unstructured":"Dhillon, G.S., Chaudhari, P., Ravichandran, A., Soatto, S.: A baseline for few-shot image classification. In: International Conference on Learning Representations (2019)"},{"key":"5_CR18","unstructured":"Doersch, C., Gupta, A., Zisserman, A.: Crosstransformers: spatially-aware few-shot transfer. In: Advances in Neural Information Processing Systems, vol. 33, pp. 21981\u201321993 (2020)"},{"issue":"4","key":"5_CR19","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1109\/TPAMI.2006.79","volume":"28","author":"L Fei-Fei","year":"2006","unstructured":"Fei-Fei, L., Fergus, R., Perona, P.: One-shot learning of object categories. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 594\u2013611 (2006)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"5_CR20","unstructured":"Fink, M.: Object classification from a single example utilizing class relevance metrics. In: Advances in Neural Information Processing Systems, vol. 17 (2004)"},{"key":"5_CR21","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. PMLR (2017)"},{"key":"5_CR22","unstructured":"Garcia, V., Bruna, J.: Few-shot learning with graph neural networks. arXiv preprint arXiv:1711.04043 (2017)"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Gidaris, S., Bursuc, A., Komodakis, N., P\u00e9rez, P., Cord, M.: Boosting few-shot visual learning with self-supervision. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8059\u20138068 (2019)","DOI":"10.1109\/ICCV.2019.00815"},{"key":"5_CR24","unstructured":"Gidaris, S., Singh, P., Komodakis, N.: Unsupervised representation learning by predicting image rotations. In: International Conference on Learning Representations (2018)"},{"issue":"11","key":"5_CR25","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139\u2013144 (2020)","journal-title":"Commun. ACM"},{"issue":"03","key":"5_CR26","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1142\/S0218213008004059","volume":"17","author":"S Gutstein","year":"2008","unstructured":"Gutstein, S., Fuentes, O., Freudenthal, E.: Knowledge transfer in deep convolutional neural nets. Int. J. Artif. Intell. Tools 17(03), 555\u2013567 (2008)","journal-title":"Int. J. Artif. Intell. Tools"},{"key":"5_CR27","doi-asserted-by":"crossref","unstructured":"Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), vol. 2, pp. 1735\u20131742. IEEE (2006)","DOI":"10.1109\/CVPR.2006.100"},{"key":"5_CR28","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"5_CR29","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"5_CR30","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 and Expo (ICME), pp. 1\u20136. IEEE (2020)","DOI":"10.1109\/ICME46284.2020.9102917"},{"key":"5_CR31","doi-asserted-by":"crossref","unstructured":"Hu, S.X., Li, D., St\u00fchmer, J., Kim, M., Hospedales, T.M.: Pushing the limits of simple pipelines for few-shot learning: external data and fine-tuning make a difference. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9068\u20139077 (2022)","DOI":"10.1109\/CVPR52688.2022.00886"},{"issue":"7","key":"5_CR32","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1007\/s00236-017-0294-5","volume":"54","author":"S Jha","year":"2017","unstructured":"Jha, S., Seshia, S.A.: A theory of formal synthesis via inductive learning. Acta Inform. 54(7), 693\u2013726 (2017). https:\/\/doi.org\/10.1007\/s00236-017-0294-5","journal-title":"Acta Inform."},{"issue":"11","key":"5_CR33","doi-asserted-by":"publisher","first-page":"4037","DOI":"10.1109\/TPAMI.2020.2992393","volume":"43","author":"L Jing","year":"2020","unstructured":"Jing, L., Tian, Y.: Self-supervised visual feature learning with deep neural networks: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 43(11), 4037\u20134058 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"5_CR34","doi-asserted-by":"crossref","unstructured":"Kim, J., Kim, T., Kim, S., Yoo, C.D.: Edge-labeling graph neural network for few-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11\u201320 (2019)","DOI":"10.1109\/CVPR.2019.00010"},{"issue":"6266","key":"5_CR35","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 350(6266), 1332\u20131338 (2015)","journal-title":"Science"},{"key":"5_CR36","doi-asserted-by":"crossref","unstructured":"Ledig, C., et al.: 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 (2017)","DOI":"10.1109\/CVPR.2017.19"},{"key":"5_CR37","doi-asserted-by":"crossref","unstructured":"Liu, C., et al.: Learning a few-shot embedding model with contrastive learning. In: AAAI (2021)","DOI":"10.1609\/aaai.v35i10.17047"},{"key":"5_CR38","unstructured":"Liu, Y., et al.: Learning to propagate labels: transductive propagation network for few-shot learning. In: International Conference on Learning Representations (2018)"},{"key":"5_CR39","doi-asserted-by":"crossref","unstructured":"Luo, X., Chen, Y., Wen, L., Pan, L., Xu, Z.: Boosting few-shot classification with view-learnable contrastive learning. In: 2021 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136. IEEE (2021)","DOI":"10.1109\/ICME51207.2021.9428444"},{"key":"5_CR40","doi-asserted-by":"crossref","unstructured":"Ma, J., Xie, H., Han, G., Chang, S.F., Galstyan, A., Abd-Almageed, W.: Partner-assisted learning for few-shot image classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10573\u201310582 (2021)","DOI":"10.1109\/ICCV48922.2021.01040"},{"key":"5_CR41","unstructured":"Mishra, N., Rohaninejad, M., Chen, X., Abbeel, P.: A simple neural attentive meta-learner. In: International Conference on Learning Representations (2018)"},{"key":"5_CR42","unstructured":"Nichol, A., Schulman, J.: Reptile: a scalable metalearning algorithm. arXiv preprint arXiv:1803.029992(3), 4 (2018)"},{"key":"5_CR43","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/978-3-319-46466-4_5","volume-title":"Computer Vision \u2013 ECCV 2016","author":"M Noroozi","year":"2016","unstructured":"Noroozi, M., Favaro, P.: Unsupervised learning of visual representations by solving jigsaw puzzles. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 69\u201384. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46466-4_5"},{"key":"5_CR44","unstructured":"Oreshkin, B., Rodr\u00edguez L\u00f3pez, P., Lacoste, A.: Tadam: task dependent adaptive metric for improved few-shot learning. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"key":"5_CR45","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1007\/978-3-030-86486-6_41","volume-title":"Machine Learning and Knowledge Discovery in Databases. Research Track","author":"Y Ouali","year":"2021","unstructured":"Ouali, Y., Hudelot, C., Tami, M.: Spatial contrastive learning for\u00a0few-shot classification. In: Oliver, N., P\u00e9rez-Cruz, F., Kramer, S., Read, J., Lozano, J.A. (eds.) ECML PKDD 2021. LNCS (LNAI), vol. 12975, pp. 671\u2013686. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-86486-6_41"},{"key":"5_CR46","doi-asserted-by":"crossref","unstructured":"Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2536\u20132544 (2016)","DOI":"10.1109\/CVPR.2016.278"},{"issue":"3","key":"5_CR47","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/s13735-021-00213-6","volume":"10","author":"M Pavan Kumar","year":"2021","unstructured":"Pavan Kumar, M., Jayagopal, P.: Multi-class imbalanced image classification using conditioned GANs. Int. J. Multimedia Inf. Retrieval 10(3), 143\u2013153 (2021)","journal-title":"Int. J. Multimedia Inf. Retrieval"},{"key":"5_CR48","unstructured":"Ren, M., et al.: Meta-learning for semi-supervised few-shot classification. In: International Conference on Learning Representations (2018)"},{"key":"5_CR49","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/978-3-030-58574-7_8","volume-title":"Computer Vision \u2013 ECCV 2020","author":"P Rodr\u00edguez","year":"2020","unstructured":"Rodr\u00edguez, P., Laradji, I., Drouin, A., Lacoste, A.: Embedding propagation: smoother manifold for few-shot classification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12371, pp. 121\u2013138. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58574-7_8"},{"issue":"1","key":"5_CR50","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1198\/106186007X181425","volume":"16","author":"JA Royle","year":"2007","unstructured":"Royle, J.A., Dorazio, R.M., Link, W.A.: Analysis of multinomial models with unknown index using data augmentation. J. Comput. Graph. Stat. 16(1), 67\u201385 (2007)","journal-title":"J. Comput. Graph. Stat."},{"key":"5_CR51","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1007\/978-3-030-58571-6_38","volume-title":"Computer Vision \u2013 ECCV 2020","author":"J-C Su","year":"2020","unstructured":"Su, J.-C., Maji, S., Hariharan, B.: When does self-supervision improve few-shot learning? In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12352, pp. 645\u2013666. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58571-6_38"},{"key":"5_CR52","doi-asserted-by":"crossref","unstructured":"Tang, X., Teng, Z., Zhang, B., Fan, J.: Self-supervised network evolution for few-shot classification. In: IJCAI, pp. 3045\u20133051 (2021)","DOI":"10.24963\/ijcai.2021\/419"},{"key":"5_CR53","doi-asserted-by":"publisher","unstructured":"Thrun, S., Pratt, L.: Learning to learn: Introduction and overview. In: Thrun, S., Pratt, L. (eds.) Learning to learn, pp. 3\u201317. Springer, Cham (1998). https:\/\/doi.org\/10.1007\/978-1-4615-5529-2_1","DOI":"10.1007\/978-1-4615-5529-2_1"},{"key":"5_CR54","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"776","DOI":"10.1007\/978-3-030-58621-8_45","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Tian","year":"2020","unstructured":"Tian, Y., Krishnan, D., Isola, P.: Contrastive multiview coding. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 776\u2013794. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58621-8_45"},{"key":"5_CR55","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1007\/978-3-030-58568-6_16","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Tian","year":"2020","unstructured":"Tian, Y., Wang, Y., Krishnan, D., Tenenbaum, J.B., Isola, P.: Rethinking few-shot image classification: a good embedding is all you need? In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 266\u2013282. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58568-6_16"},{"issue":"2","key":"5_CR56","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1023\/A:1019956318069","volume":"18","author":"R Vilalta","year":"2002","unstructured":"Vilalta, R., Drissi, Y.: A perspective view and survey of meta-learning. Artif. Intell. Rev. 18(2), 77\u201395 (2002)","journal-title":"Artif. Intell. Rev."},{"key":"5_CR57","unstructured":"Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al.: Matching networks for one shot learning. In: Advances in Neural Information Processing Systems, vol. 29 (2016)"},{"issue":"3","key":"5_CR58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3386252","volume":"53","author":"Y Wang","year":"2020","unstructured":"Wang, Y., Yao, Q., Kwok, J.T., Ni, L.M.: Generalizing from a few examples: a survey on few-shot learning. ACM Comput. Surv. (CSUR) 53(3), 1\u201334 (2020)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"5_CR59","doi-asserted-by":"crossref","unstructured":"Wei, C., et al.: Iterative reorganization with weak spatial constraints: solving arbitrary jigsaw puzzles for unsupervised representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1910\u20131919 (2019)","DOI":"10.1109\/CVPR.2019.00201"},{"key":"5_CR60","doi-asserted-by":"crossref","unstructured":"Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3733\u20133742 (2018)","DOI":"10.1109\/CVPR.2018.00393"},{"key":"5_CR61","doi-asserted-by":"crossref","unstructured":"Yan, W., Yap, J., Mori, G.: Multi-task transfer methods to improve one-shot learning for multimedia event detection. In: BMVC, pp. 37\u20131 (2015)","DOI":"10.5244\/C.29.37"},{"key":"5_CR62","doi-asserted-by":"crossref","unstructured":"Yang, L., Li, L., Zhang, Z., Zhou, X., Zhou, E., Liu, Y.: DPGN: distribution propagation graph network for few-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13390\u201313399 (2020)","DOI":"10.1109\/CVPR42600.2020.01340"},{"key":"5_CR63","doi-asserted-by":"crossref","unstructured":"Yang, Z., Wang, J., Zhu, Y.: Few-shot classification with contrastive learning. arXiv preprint arXiv:2209.08224 (2022)","DOI":"10.1007\/978-3-031-20044-1_17"},{"key":"5_CR64","doi-asserted-by":"crossref","unstructured":"Ye, H.J., Hu, H., Zhan, D.C., Sha, F.: Few-shot learning via embedding adaptation with set-to-set functions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8808\u20138817 (2020)","DOI":"10.1109\/CVPR42600.2020.00883"},{"key":"5_CR65","unstructured":"Zhang, C., Cai, Y., Lin, G., Shen, C.: DeepEMD: differentiable earth mover\u2019s distance for few-shot learning. arXiv preprint arXiv:2003.06777 (2020)"},{"key":"5_CR66","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1007\/978-3-319-46487-9_40","volume-title":"Computer Vision \u2013 ECCV 2016","author":"R Zhang","year":"2016","unstructured":"Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 649\u2013666. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46487-9_40"},{"key":"5_CR67","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/978-3-030-23367-9_2","volume-title":"Artificial Intelligence and Mobile Services \u2013 AIMS 2019","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Yang, W., Sun, W., Ye, K., Chen, M., Xu, C.-Z.: The constrained GAN with hybrid encoding in predicting financial behavior. In: Wang, D., Zhang, L.-J. (eds.) AIMS 2019. LNCS, vol. 11516, pp. 13\u201327. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-23367-9_2"},{"key":"5_CR68","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1007\/978-3-030-59605-7_11","volume-title":"Artificial Intelligence and Mobile Services \u2013 AIMS 2020","author":"F Zhuang","year":"2020","unstructured":"Zhuang, F., Ren, L., Dong, Q., Sinnott, R.O.: A mobile application using deep learning to automatically classify adult-only images. In: Xu, R., De, W., Zhong, W., Tian, L., Bai, Y., Zhang, L.-J. (eds.) AIMS 2020. LNCS, vol. 12401, pp. 140\u2013155. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59605-7_11"}],"container-title":["Lecture Notes in Computer Science","Cloud Computing \u2013 CLOUD 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-23498-9_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T09:14:31Z","timestamp":1728551671000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-23498-9_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031234972","9783031234989"],"references-count":68,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-23498-9_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"14 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CLOUD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Cloud Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Honolulu, HI","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cloud2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.servicessociety.org\/cloud","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EDAS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"15","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"53% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}