This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Hinton G E, Krizhevsky A, Wang S D. Transforming auto-encoders[C]//International Conference on Artificial Neural Networks. Springer, Berlin, Heidelberg, 2011: 44-51.HintonG EKrizhevskyAWangS DTransforming auto-encoders[C]International Conference on Artificial Neural NetworksSpringerBerlin, Heidelberg2011445110.1007/978-3-642-21735-7_6Search in Google Scholar
He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.HeKZhangXRenSDeep residual learning for image recognition[C]Proceedings of the IEEE conference on computer vision and pattern recognition201677077810.1109/CVPR.2016.90Search in Google Scholar
Hinton, Geoffrey E.; SABOUR, Sara; FROSST, Nicholas. Matrix capsules with EM routing. 2018.HintonGeoffrey E.; SABOURSara; FROSSTNicholasMatrix capsules with EM routing2018Search in Google Scholar
Kosiorek A, Sabour S, Teh Y W, et al. Stacked capsule autoencoders[C]//Advances in Neural Information Processing Systems. 2019: 15512-15522.KosiorekASabourSTehY WStacked capsule autoencoders[C]Advances in Neural Information Processing Systems20191551215522Search in Google Scholar
Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]//Advances in neural information processing systems. 2012: 1097-1105.KrizhevskyASutskeverIHintonG EImagenet classification with deep convolutional neural networks[C]//Advances in neural information processing systems201210971105Search in Google Scholar
LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.LeCunYBottouLBengioYGradient-based learning applied to document recognition[J]Proceedings of the IEEE199886112278232410.1109/5.726791Search in Google Scholar
LeCun, Yann, Corinna Cortes, and Christopher JC Burges. “The MNIST database of handwritten digits, 1998.” URL http://yann.lecun.com/exdb/mnist 10 (1998): 34.LeCunYannCorinnaCortes, and Christopher JCBurges. “The MNIST database of handwritten digits, 1998.” URL http://yann.lecun.com/exdb/mnist10199834Search in Google Scholar
Netzer, Yuval, et al. “Reading digits in natural images with unsupervised feature learning.” (2011).NetzerYuval“Reading digits in natural images with unsupervised feature learning.”2011Search in Google Scholar
Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323(6088): 533-536.RumelhartD EHintonG EWilliamsR JLearning representations by back-propagating errors[J]Nature1986323608853353610.1038/323533a0Search in Google Scholar
Sabour S, Frosst N, Hinton G E. Dynamic routing between capsules[C]//Advances in neural information processing systems. 2017: 3856-3866.SabourSFrosstNHintonG EDynamic routing between capsules[C]//Advances in neural information processing systems201738563866Search in Google Scholar
Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 1-9.SzegedyCLiuWJiaYGoing deeper with convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition20151910.1109/CVPR.2015.7298594Search in Google Scholar
Zhao W, Ye J, Yang M, et al. Investigating CapsuleNetworks with Dynamic Routing for Text Classification[J]. arXiv preprint arXiv: 1804.00538, 2018ZhaoWYeJYangMInvestigating CapsuleNetworks with Dynamic Routing for Text Classification[J]arXiv preprint arXiv: 1804.005382018Search in Google Scholar
