Haiku Generation Based on Motif Images Using Deep Learning Koki Yoneda 1 Soichiro Yokoyama 2 Tomohisa Yamashita 2 Hidenori Kawamura Scho
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1 Haiku Generation Based on Motif Images Using Deep Learning Koki Yoneda 1 Soichiro Yokoyama 2 Tomohisa Yamashita 2 Hidenori Kawamura School of Engineering Hokkaido University 2 2 Graduate School of Information Science and Technology, Hokkaido University Abstract:,.,,,, LSTM,,,.,LSTM 1 1.1,,.,Google Deep Convolution Neural Network (CNN) Deep CNN, Recurrent Neural Network (RNN)[3] RNN Google [4]., DCGAN, [5],,,17,, 1 [1][2], yoneda@complex.ist.hokudai.ac.jp,,,,,,, [6][8], [7] 1.2,,,,,,,,
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8 [1] [2] 2014 [3] Shujie Liu,Nan Yang,Mu Li,Ming Zhou. A Recursive Recurrent Neural Network for Statistical Machine Translation,2014,ACL. [4] Yonghui Wu, Mike Schuster, Zhifeng Chen, et al., Google s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation,2016,arXiv: [5] Alec Radford, Luke Metz, Soumith Chintala, Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks,2015,arXiv. [6] YANG, Ming, and Masafumi HAGIWARA. A Text-based Automatic Waka Generation System using Kansei. International Journal of Affective Engineering 15.2 (2016): [14] MIKOLOV, Tomas, et al. Efficient estimation of word representations in vector space. arxiv preprint arxiv: , [15] TensorFlow [16] Kingma, Diederik P., and Jimmy Ba. Adam: A method for stochastic optimization. arxiv preprint arxiv: (2014). [17] SZEGEDY, Christian, et al. Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition p [18] Levenshtein, Vladimir I. Binary codes capable of correcting deletions, insertions, and reversals. Soviet physics doklady. Vol. 10. No [7] ianchao Wu, Momo Klyen, Kazushige Ito, Zhan Chen Haiku Generation Using Deep Neural Networks 23 [8] Tosa, Naoko, Hideto Obara, and Michihiko Minoh. Hitch haiku: An interactive supporting system for composing haiku poem. International Conference on Entertainment Computing. Springer, Berlin, Heidelberg, [9] OPEN Hammerhead V1. [10] CC BY [11] [12] [13] Sundermeyer, Martin, Ralf Schlter, and Hermann Ney. LSTM neural networks for language modeling. Thirteenth Annual Conference of the International Speech Communication Association
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