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Committee Date Time Place Paper Title / Authors Abstract Paper #
CCS 2023-03-26
13:35		 Hokkaido RUSUTSU RESORT Analysis of learning performance in CycleGAN by applying data augmentation to few data
Syuhei Kanzaki, Hidehiro Nakano (Tokyo City Univ.) CCS2022-72		 In machine learning and deep learning, a huge amount of data is required for training. The image generation model GAN ex... [more] CCS2022-72
pp.54-58
CCS 2022-11-18
15:20		 Mie
(Primary: On-site, Secondary: Online)		 Multi-domain translation from few data by CycleGAN applying data augmentation
Syuhei Kanzaki, Hidehiro Nakano (Tokyo City Univ.) CCS2022-59		 In machine learning and deep learning, a huge amount of data is required for training. The image generation model GAN ex... [more] CCS2022-59
pp.81-84
IN, CCS
(Joint)		 2022-08-04
10:00		 Hokkaido Hokkaido University(Centennial Hall)
(Primary: On-site, Secondary: Online)		 Investigation on Applying Data Augmentation to CycleGAN
Syuhei Kanzaki, Hidehiro Nakano (Tokyo City Univ.) CCS2022-26		 In machine learning and deep learning, a huge amount of data is required for training. The image generation model GAN ex... [more] CCS2022-26
pp.1-5
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