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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 5 of 5  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
BCT, IEICE-SIS 2021-10-08
11:30
Online online Analysis of Writing Style on Wood Slips of the Chinese Han period Using Deep Generative Model
Chiang Meng Yuan, Soh Yoshida, Takao Fujita, Mitsuji Muneyasu (Kansai Univ.)
In this paper, we develop a method to objectively analyze the calligraphic styles of wood slips excavated in Northwester... [more]
IEICE-IE, IEICE-ITS, MMS, ME, AIT [detail] 2021-02-19
14:15
Online Online [Special Talk] A Note on Electron Microscope Image Generation from Mix Proportion via Conditional Style Generative Adversarial Network for Rubber Materials
Rintaro Yanagi, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.)
Estimating the properties of rubber materials from ingredients is necessary to accelerate rubber material development. I... [more] MMS2021-22 ME2021-22 AIT2021-22
pp.171-175
HI, IEICE-IE, IEICE-ITS, MMS, ME, AIT [detail] 2020-02-27
16:20
Hokkaido Hokkaido Univ.
(Cancelled)
A Note on Generation of Electron Microscope Images via Auxiliary Classifier Generative Adversarial Network with Mix Proportions
Misaki Kanai, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.)
In this paper, we investigate a method for generation of images that represent the internal structure of rubber material... [more] MMS2020-21 HI2020-21 ME2020-49 AIT2020-21
pp.107-111
HI, IEICE-IE, IEICE-ITS, MMS, ME, AIT [detail] 2020-02-28
15:10
Hokkaido Hokkaido Univ.
(Cancelled)
Unpaired Learning for Noise-free, Scale Invariant, and Interpretable Image Enhancement
Satoshi Kosugi, Toshihiko Yamasaki (Univ. of Tokyo)
This paper tackles unpaired image enhancement, a task of learning a mapping function which transforms input images into ... [more]
IEICE-ITS, IEICE-IE, MMS, HI, ME, AIT [detail] 2018-02-16
10:45
Hokkaido Hokkaido Univ. A Note on Use of Generative Adversarial Networks for Gastritis Classification from Gastric X-ray Images
Ren Togo, Kenta Ishihara, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.)
This paper presents potential of gastritis images generated by generative adversarial networks (GANs) for gastritis clas... [more] MMS2018-31 HI2018-31 ME2018-31 AIT2018-31
pp.299-303
 Results 1 - 5 of 5  /   
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