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All Technical Committee Conferences (Searched in: All Years)
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Search Results: Conference Papers |
Conference Papers (Available on Advance Programs) (Sort by: Date Descending) |
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Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
EA |
2019-12-12 14:25 |
Fukuoka |
Kyushu Inst. Tech. |
Performance improvement of speech enhancement network by multitask learning including noise information Haruki Tanaka (NITTC), Yosuke Sugiura, Nozomiko Yasui, Tetsuya Shimamura (Saitama Univ.), Ryoichi Miyazaki (NITTC) EA2019-70 |
In the signal processing field, there is a growing interest in speech enhancement.Recently, a lot of speech enhancement ... [more] |
EA2019-70 pp.31-36 |
PRMU, BioX |
2019-03-18 10:00 |
Tokyo |
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A Generative Self-Ensemble Approach to Simulated+Unsupervised Learning Yu Mitsuzumi (Kyoto Univ.), Go Irie (NTT), Atsushi Nakazawa (Kyoto Univ.), Akisato Kimura (NTT) BioX2018-52 PRMU2018-156 |
The simulated and unsupervised (S+U) learning framework is an effective approach in computer vision since it solves vari... [more] |
BioX2018-52 PRMU2018-156 pp.137-142 |
CCS |
2018-03-26 10:00 |
Tokyo |
Tokyo Univ. of Sci. (Morito Memorial Hall) |
Suppression Method of Mode Collapse in Generative Adversarial Nets Shinya Hidai, Hidehiro Nakano, Arata Miyauchi (Tokyo City Univ.) CCS2017-33 |
Generative Adversarial Nets (GAN) is constituted by two neural networks, Generator and Discrminator. Generator creates d... [more] |
CCS2017-33 pp.1-6 |
PRMU |
2017-10-12 15:10 |
Kumamoto |
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[Tutorial Lecture]
Families of GANs Tomohiro Takahashi (ABEJA) PRMU2017-80 |
Generative Adversarial Networks(GANs) have recently gained popularity due to their ability to synthesize images which ar... [more] |
PRMU2017-80 pp.95-100 |
PRMU |
2017-10-13 11:10 |
Kumamoto |
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PRMU2017-87 |
Generative Adversarial Nets (GANs) is a pair of neural networks which can learn data distribution and generate various d... [more] |
PRMU2017-87 pp.139-144 |
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