| Presentation | 2018-01-19 Automatic biometric data generation by Generative Adversarial Network and its application to deep learning sample Tatsuya Takahata, Kazumasa Horie, Yuri Akizuki, Soichiro Ikuno, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | While deep learning has high learning performance and recognition performance, it requires a large amount of sample data for learning. However, it takes time to collect daily personal biological data such as body temperature. Therefore, it is difficult to adopt the biological data as learning data of the deep learning at the early stage of collection. In the present study, the virtual learning data for the deep learning are generated by Generative Adversarial Network (GAN) using data at the early stage of collection. Additionally, the generated data are adopted for learning data of the deep learning. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Deep Learning / GAN / Data Analysis / Biological Data / Automatically Generated |
| Paper # | MoNA2017-55 |
| Date of Issue | 2018-01-11 (MoNA) |
| Conference Information | |
| Committee | MoNA |
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| Conference Date | 2018/1/18(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Campus Plaza Kyoto |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Mobile Network, Application of Machine Learning, Mobile Data, etc. |
| Chair | Ryoichi Shinkuma(Kyoto Univ.) |
| Vice Chair | Shigeaki Tagashira(Kansai Univ.) / Gen Kitagata(Tohoku Univ.) |
| Secretary | Shigeaki Tagashira(Kyushu Univ.) / Gen Kitagata(NTT) |
| Assistant | Takayuki Nishio(Kyoto Univ.) / Takato Saito(NTT) |
| Paper Information | |
| Registration To | Technical Committee on Mobile Network and Applications |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Automatic biometric data generation by Generative Adversarial Network and its application to deep learning sample |
| Sub Title (in English) | |
| Keyword(1) | Deep Learning |
| Keyword(2) | GAN |
| Keyword(3) | Data Analysis |
| Keyword(4) | Biological Data |
| Keyword(5) | Automatically Generated |
| 1st Author's Name | Tatsuya Takahata |
| 1st Author's Affiliation | Tokyo University of Technology(Tokyo University of Technology) |
| 2nd Author's Name | Kazumasa Horie |
| 2nd Author's Affiliation | University of Tsukuba(University of Tsukuba) |
| 3rd Author's Name | Yuri Akizuki |
| 3rd Author's Affiliation | Kumamoto University(Kumamoto Univ.) |
| 4th Author's Name | Soichiro Ikuno |
| 4th Author's Affiliation | Tokyo University of Technology(Tokyo University of Technology) |
| Date | 2018-01-19 |
| Paper # | MoNA2017-55 |
| Volume (vol) | vol.117 |
| Number (no) | MoNA-390 |
| Page | pp.pp.79-82(MoNA), |
| #Pages | 4 |
| Date of Issue | 2018-01-11 (MoNA) |