{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T02:41:24Z","timestamp":1722912084191},"reference-count":32,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Fundamentals"],"published-print":{"date-parts":[[2021,8,1]]},"DOI":"10.1587\/transfun.2020eap1101","type":"journal-article","created":{"date-parts":[[2021,1,26]],"date-time":"2021-01-26T22:06:40Z","timestamp":1611698800000},"page":"1043-1050","source":"Crossref","is-referenced-by-count":3,"title":["Capsule Network with Shortcut Routing"],"prefix":"10.1587","volume":"E104.A","author":[{"given":"Thanh Vu","family":"DANG","sequence":"first","affiliation":[{"name":"Department of ICT Convergence System Engineering, Chonnam National University"}]},{"given":"Hoang Trong","family":"VO","sequence":"additional","affiliation":[{"name":"Department of ICT Convergence System Engineering, Chonnam National University"}]},{"given":"Gwang Hyun","family":"YU","sequence":"additional","affiliation":[{"name":"Department of ICT Convergence System Engineering, Chonnam National University"}]},{"given":"Jin Young","family":"KIM","sequence":"additional","affiliation":[{"name":"Department of ICT Convergence System Engineering, Chonnam National University"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] G.E. Hinton, A. Krizhvesky, and S.D. Wang, \u201cTransforming auto-encoders,\u201d International Conference on Artificial Neural Networks-ICANN 2011, Berlin, 2011. 10.1007\/978-3-642-21735-7_6","DOI":"10.1007\/978-3-642-21735-7_6"},{"key":"2","unstructured":"[2] G.E. Hinton, S. Sabour, and N. Frosst, \u201cMatrix capsules with EM routing,\u201d International Conference on Learning Representations, 2018."},{"key":"3","unstructured":"[3] S. Sabour, N. Frosst, and G.E. Hinton, \u201cDynamic routing between capsules,\u201d Advances in Neural Information Processing Systems, pp.3856-3866, 2017."},{"key":"4","unstructured":"[4] A. Kosiorek, S. Sabour, Y.W. Teh, and G.E. Hinton, \u201cStacked capsule autoencoders,\u201d Advances in Neural Information Processing Systems, pp.15486-15496, 2019."},{"key":"5","unstructured":"[5] G.E. Hinton, Z. Ghahramani, and W.Y. Teh, \u201cLearning to parse images,\u201d Advances in Neural Information Processing Systems 12, 1999."},{"key":"6","unstructured":"[6] R.S. Zemel, C.M. Mozer, and G.E. Hinton, \u201cTRAFFIC: Recognizing objects using hierarchical reference frame transformations,\u201d Advances in Neural Information Processing Systems 2, 1989."},{"key":"7","unstructured":"[7] I. Goodfellow, Y. Bengio, and A. Courville, \u201cManifold Learning,\u201d Deep Learning, pp.156-159, The MIT Press, London, 2017."},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] Y. Bengio, A. Courville, and P. Vincent, \u201cRepresentation learning: A review and new perspectives,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.35, no.8, pp.1798-1828, 2013. 10.1109\/tpami.2013.50","DOI":"10.1109\/TPAMI.2013.50"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] K. He, X. Zhang, S. Ren, and J. Sun, \u201cDeep residual learning for image recognition,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2016. 10.1109\/cvpr.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"10","unstructured":"[10] T.D. Kulkarni, W. Whitney, P. Kohli, and J.B. Tenenbaum, \u201cDeep convolutional inverse graphics network,\u201d Advances in Neural Information Processing Systems, pp.2539-2547, 2015."},{"key":"11","unstructured":"[11] M. Jaderberg, K. Simonyan, and A. Zisserman, \u201cSpatial transformer networks,\u201d Advances in Neural Information Processing Systems, pp.2017-2025, 2015."},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] D.E. Worrall, S.J. Garbin, D. Turmukhambetov, and G. Brostow, \u201cHarmonic networks: Deep translation and rotation equivariance,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2017. 10.1109\/cvpr.2017.758","DOI":"10.1109\/CVPR.2017.758"},{"key":"13","unstructured":"[13] I. Goodfellow, Y. Bengio, and A. Courville, \u201cAdversarial Training,\u201d Deep Learning, pp.261-263, The MIT Press, London, 2017."},{"key":"14","unstructured":"[14] I.J. Goodfellow, J. Shlens, and C. Szegedy, Explaining and Harnessing Adversarial Examples, arXiv preprint arXiv:1412.6572, 2014."},{"key":"15","unstructured":"[15] J.E. Lenssen, M. Fey, and P. Libuschewski, \u201cGroup equivariant capsule networks,\u201d Advances in Neural Information Processing Systems, pp.8844-8853, 2018."},{"key":"16","unstructured":"[16] T.S. Cohen and M. Welling, \u201cGroup equivariant convolutional networks,\u201d International Conference on Machine Learning, 2016."},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] J. Choi, H. Seo, S. Im, and M. Kang, \u201cAttention routing between capsules,\u201d Proc. IEEE International Conference on Computer Vision Workshops, 2019. 10.1109\/iccvw.2019.00247","DOI":"10.1109\/ICCVW.2019.00247"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] H. Li, X. Guo, B. Dai, W. Ouyang, and X. Wang, \u201cNeural network encapsulation,\u201d Proc. European Conference on Computer Vision (ECCV), 2018. 10.1007\/978-3-030-01252-6_16","DOI":"10.1007\/978-3-030-01252-6_16"},{"key":"19","unstructured":"[19] K. Ahmed and L. Torresani, \u201cSTAR-CAPS: Capsule networks with straight-through attentive routing,\u201d Advances in Neural Information Processing Systems, pp.9098-9107, 2019."},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] S. Zhang, W. Zhao, X. Wu, and Q. Zhou, \u201cFast dynamic routing based on weighted kernel density estimation,\u201d International Symposium on Artificial Intelligence and Robotics, pp.301-309, 2018. 10.1007\/978-3-030-04946-1_30","DOI":"10.1007\/978-3-030-04946-1_30"},{"key":"21","unstructured":"[21] K. Duarte, Y.S. Rawat, and M. Shah, \u201cVideoCapsuleNet: A simplified network for action detection,\u201d arXiv preprint arXiv:1805.08162, 2018."},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] J. Rajasegaran, V. Jayasundara, S. Jayasekara, H. Jayasekara, S. Seneviratne, and R. Rodrigo, \u201cDeepCaps: Going deeper with capsule networks,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2019. 10.1109\/cvpr.2019.01098","DOI":"10.1109\/CVPR.2019.01098"},{"key":"23","unstructured":"[23] L. Zhang, M. Edraki, and G.J. Qi, \u201cCapProNet: Deep feature learning via orthogonal projections onto capsule subspaces,\u201d Advances in Neural Information Processing Systems, pp.5814-5823, 2018."},{"key":"24","unstructured":"[24] R. LaLonde and U. Bagci, \u201cCapsules for object segmentation,\u201d arXiv preprint arXiv:1804.04241, 2018."},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] P. Afshar, A. Mohammadi, and P. Konstantinos, \u201cBrain tumor type classification via capsule networks,\u201d 2018 25th IEEE International Conference on Image Processing (ICIP), 2018. 10.1109\/icip.2018.8451379","DOI":"10.1109\/ICIP.2018.8451379"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] T. Iesmantas and R. Alzbutas, \u201cConvolutional capsule network for classification of breast cancer histology images,\u201d International Conference Image Analysis and Recognition, 2018. 10.1007\/978-3-319-93000-8_97","DOI":"10.1007\/978-3-319-93000-8_97"},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] X. Zhang, P. Li, W. Jia, and H. Zhao, \u201cMulti-labeled Relation Extraction with attentive capsule network,\u201d arXiv preprint arXiv:1811.04354, 2018.","DOI":"10.1609\/aaai.v33i01.33017484"},{"key":"28","unstructured":"[28] W. Zhao, J. Ye, M. Yang, Z. Lei, S. Zhang, and Z. Zhao, \u201cInvestigating capsule networks with dynamic routing for text classification,\u201d arXiv preprint arXiv:1804.00538, 2018."},{"key":"29","doi-asserted-by":"crossref","unstructured":"[29] Y. Zhao, T. Birdal, H. Deng, and F. Tombari, \u201c3D point capsule networks,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2019. 10.1109\/cvpr.2019.00110","DOI":"10.1109\/CVPR.2019.00110"},{"key":"30","unstructured":"[30] A.G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, \u201cMobileNets: Efficient convolutional neural networks for mobile vision applications,\u201d arXiv preprint arXiv:1704.04861, 2017."},{"key":"31","unstructured":"[31] L. Yann, J.F. Huan, and L. Bottou, \u201cLearning methods for generic object recognition with invariance to pose and lighting,\u201d IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. 10.1109\/cvpr.2004.1315150"},{"key":"32","doi-asserted-by":"publisher","unstructured":"[32] C. Xiang, L. Zhang, Y. Tang, W. Zou, and C. Xu, \u201cMS-CapsNet: A novel multi-scale capsule network,\u201d IEEE Signal Process. Lett., vol.25, no.12, pp.1850-1854, 2018. 10.1109\/lsp.2018.2873892","DOI":"10.1109\/LSP.2018.2873892"}],"container-title":["IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E104.A\/8\/E104.A_2020EAP1101\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,7]],"date-time":"2021-08-07T05:17:34Z","timestamp":1628313454000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E104.A\/8\/E104.A_2020EAP1101\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,1]]},"references-count":32,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2021]]}},"URL":"https:\/\/doi.org\/10.1587\/transfun.2020eap1101","relation":{},"ISSN":["0916-8508","1745-1337"],"issn-type":[{"value":"0916-8508","type":"print"},{"value":"1745-1337","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,1]]},"article-number":"2020EAP1101"}}