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 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   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   [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   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
 Results 1 - 5 of 5  /   
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