{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T17:58:11Z","timestamp":1772647091188,"version":"3.50.1"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,5,20]],"date-time":"2023-05-20T00:00:00Z","timestamp":1684540800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,5,20]],"date-time":"2023-05-20T00:00:00Z","timestamp":1684540800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004281","name":"Narodowe Centrum Nauki","doi-asserted-by":"publisher","award":["2022\/45\/N\/ST6\/03374"],"award-info":[{"award-number":["2022\/45\/N\/ST6\/03374"]}],"id":[{"id":"10.13039\/501100004281","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004281","name":"Narodowe Centrum Nauki","doi-asserted-by":"publisher","award":["2019\/33\/B\/ST6\/00894"],"award-info":[{"award-number":["2019\/33\/B\/ST6\/00894"]}],"id":[{"id":"10.13039\/501100004281","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004281","name":"Narodowe Centrum Nauki","doi-asserted-by":"publisher","award":["2020\/37\/B\/ST6\/03463"],"award-info":[{"award-number":["2020\/37\/B\/ST6\/03463"]}],"id":[{"id":"10.13039\/501100004281","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Vision and Applications"],"published-print":{"date-parts":[[2023,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Few-shot models aim at making predictions using a minimal number of labeled examples from a given task. The main challenge in this area is the <jats:italic>one-shot<\/jats:italic> setting, where only one element represents each class. We propose the general framework for few-shot learning via kernel HyperNetworks\u2014the fusion of kernels and hypernetwork paradigm. Firstly, we introduce the classical realization of this framework, dubbed HyperShot. Compared to reference approaches that apply a gradient-based adjustment of the parameters, our models aim to switch the classification module parameters depending on the task\u2019s embedding. In practice, we utilize a hypernetwork, which takes the aggregated information from support data and returns the classifier\u2019s parameters handcrafted for the considered problem. Moreover, we introduce the kernel-based representation of the support examples delivered to hypernetwork to create the parameters of the classification module. Consequently, we rely on relations between the support examples\u2019 embeddings instead of the backbone models\u2019 direct feature values. Thanks to this approach, our model can adapt to highly different tasks. While such a method obtains very good results, it is limited by typical problems such as poorly quantified uncertainty due to limited data size. We further show that incorporating Bayesian neural networks into our general framework, an approach we call BayesHyperShot, solves this issue.<\/jats:p>","DOI":"10.1007\/s00138-023-01403-4","type":"journal-article","created":{"date-parts":[[2023,5,20]],"date-time":"2023-05-20T04:02:16Z","timestamp":1684555336000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["The general framework for few-shot learning by kernel HyperNetworks"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8741-6919","authenticated-orcid":false,"given":"Marcin","family":"Sendera","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcin","family":"Przewi\u0229\u017alikowski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan","family":"Miksa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mateusz","family":"Rajski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Konrad","family":"Karanowski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maciej","family":"Zi\u0229ba","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jacek","family":"Tabor","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Przemys\u0142aw","family":"Spurek","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,20]]},"reference":[{"key":"1403_CR1","first-page":"16108","volume":"33","author":"M Patacchiola","year":"2020","unstructured":"Patacchiola, M., Turner, J., Crowley, E.J., O\u2019Boyle, M., Storkey, A.J.: Bayesian meta-learning for the few-shot setting via deep kernels. Adv. Neural Inf. Process. Syst. 33, 16108\u201316118 (2020)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1403_CR2","first-page":"10285","volume":"34","author":"M Sendera","year":"2021","unstructured":"Sendera, M., Tabor, J., Nowak, A., Bedychaj, A., Patacchiola, M., Trzcinski, T., Spurek, P., Zieba, M.: Non-gaussian gaussian processes for few-shot regression. Adv. Neural Inf. Process. Syst. 34, 10285\u201310298 (2021)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1403_CR3","first-page":"13230","volume":"34","author":"Z Wang","year":"2021","unstructured":"Wang, Z., Miao, Z., Zhen, X., Qiu, Q.: Learning to learn dense gaussian processes for few-shot learning. Adv. Neural Inf. Process. Syst. 34, 13230\u201313241 (2021)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1403_CR4","unstructured":"Ha, D., Dai, A.M., Le, Q.V.: Hypernetworks. In: International Conference on Learning Representations (2017). https:\/\/openreview.net\/forum?id=rkpACe1lx"},{"key":"1403_CR5","doi-asserted-by":"crossref","unstructured":"Qiao, S., Liu, C., Shen, W., Yuille, A.L.: Few-shot image recognition by predicting parameters from activations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7229\u20137238 (2018)","DOI":"10.1109\/CVPR.2018.00755"},{"key":"1403_CR6","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: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8808\u20138817 (2020)","DOI":"10.1109\/CVPR42600.2020.00883"},{"key":"1403_CR7","unstructured":"Zhmoginov, A., Sandler, M., Vladymyrov, M.: Hypertransformer: Model generation for supervised and semi-supervised few-shot learning. In: International Conference on Machine Learning, pp. 27075\u201327098. PMLR (2022)"},{"key":"1403_CR8","doi-asserted-by":"publisher","unstructured":"Zhu, Z., Wang, L., Guo, S., Wu, G.: A Closer Look at Few-Shot Video Classification: A New Baseline and Benchmark. arXiv (2021). https:\/\/doi.org\/10.48550\/ARXIV.2110.12358. arXiv:2110.12358","DOI":"10.48550\/ARXIV.2110.12358"},{"key":"1403_CR9","doi-asserted-by":"publisher","unstructured":"Perrett, T., Masullo, A., Burghardt, T., Mirmehdi, M., Damen, D.: Temporal-relational crosstransformers for few-shot action recognition. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 475\u2013484 (2021). https:\/\/doi.org\/10.1109\/CVPR46437.2021.00054","DOI":"10.1109\/CVPR46437.2021.00054"},{"key":"1403_CR10","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 (2019). https:\/\/openreview.net\/forum?id=HkxLXnAcFQ"},{"issue":"3","key":"1403_CR11","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":"1403_CR12","unstructured":"Sheikh, A.-S., Rasul, K., Merentitis, A., Bergmann, U.: Stochastic maximum likelihood optimization via hypernetworks. arXiv preprint arXiv:1712.01141 (2017)"},{"key":"1403_CR13","doi-asserted-by":"crossref","unstructured":"Bowman, S.R., Vilnis, L., Vinyals, O., Dai, A.M., Jozefowicz, R., Bengio, S.: Generating sentences from a continuous space. In: 20th SIGNLL Conference on Computational Natural Language Learning, CoNLL 2016, pp. 10\u201321. Association for Computational Linguistics (ACL) (2016)","DOI":"10.18653\/v1\/K16-1002"},{"key":"1403_CR14","unstructured":"Bengio, S., Bengio, Y., Cloutier, J., Gescei, J.: On the optimization of a synaptic learning rule. In: Optimality in Biological and Artificial Networks, pp. 281\u2013303 (2013)"},{"issue":"9","key":"1403_CR15","first-page":"5149","volume":"44","author":"T Hospedales","year":"2021","unstructured":"Hospedales, T., Antoniou, A., Micaelli, P., Storkey, A.: Meta-learning in neural networks: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(9), 5149\u20135169 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"1403_CR16","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1162\/neco.1992.4.1.131","volume":"4","author":"J Schmidhuber","year":"1992","unstructured":"Schmidhuber, J.: Learning to control fast-weight memories: an alternative to dynamic recurrent networks. Neural Comput. 4(1), 131\u2013139 (1992). https:\/\/doi.org\/10.1162\/neco.1992.4.1.131","journal-title":"Neural Comput."},{"key":"1403_CR17","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2020.3004555","author":"F Zhuang","year":"2020","unstructured":"Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., He, Q.: A comprehensive survey on transfer learning. Proc. IEEE (2020). https:\/\/doi.org\/10.1109\/JPROC.2020.3004555","journal-title":"Proc. IEEE"},{"key":"1403_CR18","first-page":"3630","volume":"29","author":"O Vinyals","year":"2016","unstructured":"Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al.: Matching networks for one shot learning. Adv. Neural. Inf. Process. Syst. 29, 3630\u20133638 (2016)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1403_CR19","unstructured":"Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"1403_CR20","unstructured":"Oreshkin, B., Rodr\u00edguez\u00a0L\u00f3pez, P., Lacoste, A.: Tadam: Task dependent adaptive metric for improved few-shot learning. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31 (2018). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2018\/file\/66808e327dc79d135ba18e051673d906-Paper.pdf"},{"key":"1403_CR21","doi-asserted-by":"crossref","unstructured":"Hu, Y., Gripon, V., Pateux, S.: Leveraging the feature distribution in transfer-based few-shot learning. In: Artificial Neural Networks and Machine Learning\u2013ICANN 2021: 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14\u201317, 2021, Proceedings, Part II 30, pp. 487\u2013499. Springer (2021)","DOI":"10.1007\/978-3-030-86340-1_39"},{"key":"1403_CR22","doi-asserted-by":"crossref","unstructured":"Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H., Hospedales, T.M.: Learning to compare: Relation network for few-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1199\u20131208 (2018)","DOI":"10.1109\/CVPR.2018.00131"},{"key":"1403_CR23","doi-asserted-by":"crossref","unstructured":"Lee, K., Maji, S., Ravichandran, A., Soatto, S.: Meta-learning with differentiable convex optimization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10657\u201310665 (2019)","DOI":"10.1109\/CVPR.2019.01091"},{"key":"1403_CR24","unstructured":"Li, Z., Zhou, F., Chen, F., Li, H.: Meta-sgd: Learning to learn quickly for few-shot learning. arXiv preprint arXiv:1707.09835 (2017)"},{"key":"1403_CR25","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":"1403_CR26","unstructured":"Nichol, A., Achiam, J., Schulman, J.: On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999 (2018)"},{"key":"1403_CR27","unstructured":"Antoniou, A., Edwards, H., Storkey, A.: How to train your MAML. In: International Conference on Learning Representations (2019). https:\/\/openreview.net\/forum?id=HJGven05Y7"},{"key":"1403_CR28","unstructured":"Fan, C., Ram, P., Liu, S.: Sign-maml: Efficient model-agnostic meta-learning by signsgd. In: 5th Workshop on Meta-Learning at NeurIPS 2021 (2021)"},{"key":"1403_CR29","unstructured":"Ye, H.-J., Chao, W.-L.: How to train your MAML to excel in few-shot classification. In: International Conference on Learning Representations (2022). https:\/\/openreview.net\/forum?id=49h_IkpJtaE"},{"key":"1403_CR30","first-page":"113","volume":"32","author":"A Rajeswaran","year":"2019","unstructured":"Rajeswaran, A., Finn, C., Kakade, S.M., Levine, S.: Meta-learning with implicit gradients. Adv. Neural. Inf. Process. Syst. 32, 113\u2013124 (2019)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1403_CR31","unstructured":"Przewi\u0119\u017alikowski, M., Przybysz, P., Tabor, J., Zi\u0119ba, M., Spurek, P.: Hypermaml: Few-shot adaptation of deep models with hypernetworks. arXiv preprint arXiv:2205.15745 (2022)"},{"key":"1403_CR32","unstructured":"Bauer, M., Rojas-Carulla, M., \u015awi\u0105tkowski, J.B., Sch\u00f6lkopf, B., Turner, R.E.: Discriminative k-shot learning using probabilistic models. arXiv preprint arXiv:1706.00326 (2017)"},{"key":"1403_CR33","unstructured":"Garnelo, M., Rosenbaum, D., Maddison, C., Ramalho, T., Saxton, D., Shanahan, M., Teh, Y.W., Rezende, D., Eslami, S.M.A.: Conditional neural processes. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80, pp. 1704\u20131713 (2018)"},{"key":"1403_CR34","unstructured":"Yoon, J., Kim, T., Dia, O., Kim, S., Bengio, Y., Ahn, S.: Bayesian model-agnostic meta-learning. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems, pp. 7343\u20137353 (2018)"},{"key":"1403_CR35","unstructured":"Grant, E., Finn, C., Levine, S., Darrell, T., Griffiths, T.: Recasting gradient-based meta-learning as hierarchical bayes. In: International Conference on Learning Representations (2018)"},{"key":"1403_CR36","unstructured":"Borycki, P., Kubacki, P., Przewi\u0119\u017alikowski, M., Ku\u015bmierczyk, T., Tabor, J., Spurek, P.: Hypernetwork approach to Bayesian maml. arXiv preprint arXiv:2210.02796 (2022)"},{"key":"1403_CR37","unstructured":"Ravi, S., Beatson, A.: Amortized Bayesian meta-learning. In: International Conference on Learning Representations (2018)"},{"key":"1403_CR38","doi-asserted-by":"crossref","unstructured":"Nguyen, C., Do, T.-T., Carneiro, G.: Uncertainty in model-agnostic meta-learning using variational inference. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 3090\u20133100 (2020)","DOI":"10.1109\/WACV45572.2020.9093536"},{"key":"1403_CR39","unstructured":"Jerfel, G., Grant, E., Griffiths, T.L., Heller, K.: Reconciling meta-learning and continual learning with online mixtures of tasks. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems, pp. 9122\u20139133 (2019)"},{"key":"1403_CR40","unstructured":"Gordon, J., Bronskill, J., Bauer, M., Nowozin, S., Turner, R.: Meta-learning probabilistic inference for prediction. In: International Conference on Learning Representations (2018)"},{"key":"1403_CR41","doi-asserted-by":"crossref","unstructured":"Rasmussen, C.E.: Gaussian processes in machine learning. In: Summer School on Machine Learning, pp. 63\u201371. Springer (2003)","DOI":"10.1007\/978-3-540-28650-9_4"},{"key":"1403_CR42","unstructured":"Shen, X., Xiao, Y., Hu, S.X., Sbai, O., Aubry, M.: Re-ranking for image retrieval and transductive few-shot classification. In: Advances in Neural Information Processing Systems, vol. 34, pp. 25932\u201325943 (2021)"},{"key":"1403_CR43","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 International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00815"},{"key":"1403_CR44","unstructured":"Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The Caltech-UCSD Birds-200-2011 Dataset. Technical Report CNS-TR-2011-001, California Institute of Technology (2011)"},{"key":"1403_CR45","unstructured":"Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning. In: ICLR (2017)"},{"key":"1403_CR46","unstructured":"Rajasegaran, J., Khan, S.H., Hayat, M., Khan, F.S., Shah, M.: Meta-learning the learning trends shared across tasks. CoRR abs\/2010.09291 (2020)"},{"key":"1403_CR47","doi-asserted-by":"crossref","unstructured":"Sendera, M., Przewi\u0119\u017alikowski, M., Karanowski, K., Zi\u0119ba, M., Tabor, J., Spurek, P.: Hypershot: Few-shot learning by kernel hypernetworks. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2469\u20132478 (2023)","DOI":"10.1109\/WACV56688.2023.00250"},{"key":"1403_CR48","unstructured":"Snell, J., Zemel, R.: Bayesian few-shot classification with one-vs-each p\u00f3lya-gamma augmented gaussian processes. In: International Conference on Learning Representations (2020)"},{"key":"1403_CR49","doi-asserted-by":"crossref","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: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XIV 16, pp. 266\u2013282. Springer (2020)","DOI":"10.1007\/978-3-030-58568-6_16"},{"key":"1403_CR50","doi-asserted-by":"crossref","unstructured":"Rizve, M.N., Khan, S., Khan, F.S., Shah, M.: Exploring complementary strengths of invariant and equivariant representations for few-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10836\u201310846 (2021)","DOI":"10.1109\/CVPR46437.2021.01069"},{"key":"1403_CR51","doi-asserted-by":"crossref","unstructured":"Jian, Y., Torresani, L.: Label hallucination for few-shot classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 7005\u20137014 (2022)","DOI":"10.1609\/aaai.v36i6.20659"},{"issue":"3","key":"1403_CR52","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"1403_CR53","unstructured":"Lake, B., Salakhutdinov, R., Gross, J., Tenenbaum, J.: One shot learning of simple visual concepts. In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 33 (2011)"},{"key":"1403_CR54","doi-asserted-by":"publisher","unstructured":"Cohen, G., Afshar, S., Tapson, J., van Schaik, A.: Emnist: Extending mnist to handwritten letters. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 2921\u20132926 (2017). https:\/\/doi.org\/10.1109\/IJCNN.2017.7966217","DOI":"10.1109\/IJCNN.2017.7966217"},{"key":"1403_CR55","unstructured":"Kingma, D., Ba, J.: Adam: A method for stochastic optimization. In: International Conference on Learning Representations (2014)"}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-023-01403-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-023-01403-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-023-01403-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,20]],"date-time":"2023-07-20T19:04:38Z","timestamp":1689879878000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-023-01403-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,20]]},"references-count":55,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["1403"],"URL":"https:\/\/doi.org\/10.1007\/s00138-023-01403-4","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"value":"0932-8092","type":"print"},{"value":"1432-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,20]]},"assertion":[{"value":"17 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 April 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 April 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 May 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"53"}}