References
- I. T. Kim and K. J. Yoo, "Effects of augmented reality picture book on the language expression and flow of young children's in picture book reading activities," The Journal of Korea Open Association for Early Childhood Education, vol. 23, no. 1, pp. 83-109, 2018. https://doi.org/10.20437/koaece23-1-04
- K. M. Ryu, H. J. Kim, H. J. Kim, E. J. Lee, and J. Y. Heo, "A development of interactive storybook with digital board and smart device," in Proceedings of the HCI Society of Korea, Pyeongchang, Korea, 2017, pp. 1179-1182.
- Y. Kim and H. Park, "Study on the relation between young children's smart device immersion tendency and their playfulness," Early Childhood Education Research & Review, vol. 20, no. 4, pp. 337-353, 2016.
- I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, "Generative adversarial nets," Advances in Neural Information Processing Systems, vol. 27, pp. 2672-2680, 2014.
- R. Tachibana, T. Matsubara, and K. Uehara, "Semi-supervised learning using adversarial networks," in Proceedings of 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), Okayama, Japan, 2016, pp. 1-6.
- M. S. Ko, H. K. Roh, and K. H. Lee, "GANMOOK: generative adversarial network to stylize images like ink wash painting," in Proceedings of the Korea Computer Congress, 2017, pp. 793-795.
- L. C. Yang, S. Y. Chou, and Y. H. Yang, "MidiNet: a convolutional generative adversarial network for symbolic-domain music generation," in Proceedings of the 18th International Society of Music Information Retrieval Conference, Suzhou, China, 2017, pp. 324-331.
- G. C. Lee and J. Yoo, "Development an Android based OCR application for Hangul food menu," Journal of the Korea Institute of Information and Communication Engineering, vol. 21, no. 5, pp. 951-959, 2017. https://doi.org/10.6109/jkiice.2017.21.5.951
- R. Smith, "An overview of the Tesseract OCR engine," in Proceedings of the 9th International Conference on Document Analysis and Recognition (ICDAR), 2007, Parana, Brazil, pp, 629-633.
- A. C. Rodriguez, T. Kacprzak, A. Lucchi, A. Amara, R. Sgier, J. Fluri, T. Hofmann, and A. Refregier, "Fast cosmic web simulations with generative adversarial networks," Computational Astrophysics and Cosmology, vol. 5, article no. 4, 2018. https://doi.org/10.1186/s40668-018-0027-3
- R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, "Convolutional neural networks: an overview and application in radiology," Insights into Imaging, vol. 9, no. 4, pp. 611-629, 2018. https://doi.org/10.1007/s13244-018-0639-9
- A. Radford, L. Metz, and S. Chintala, "Unsupervised representation learning with deep convolutional generative adversarial networks," 2015 [Online]. Available: https://arxiv.org/abs/1511.06434.
- Y. Han and H. J. Kim, "Face morphing using generative adversarial networks," Journal of Digital Contents Society, vol. 19, no. 3, pp. 435-443, 2018.
- S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee, "Generative adversarial text to image synthesis," in Proceedings of the 33nd International Conference on Machine Learning (ICML), New York, NY, 2016, pp. 1060-1069.
- J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller, "Striving for simplicity: the all convolutional net," 2014 [Online]. Available: https://arxiv.org/abs/1412.6806.
- D. Triantafyllidou and A. Tefas, "Face detection based on deep convolutional neural networks exploiting incremental facial part learning," in Proceeding of 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico, 2016, pp, 3560-3565.
- E. Learned-Miller, G. B. Huang, A. RoyChowdhury, H. Li, and G. Hua, "Labeled faces in the wild: a survey," Advances in Face Detection and Facial Image Analysis. Cham, Switzerland: Springer, 2016, pp. 189-248.
- Y. Susanti, T. Tokunaga, H. Nishikawa, and H. Obari, "Automatic distractor generation for multiple-choice English vocabulary questions," Research and Practice in Technology Enhanced Learning, vol. 13, article no. 15, 2018.