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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 44  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
MI 2022-01-26
13:00
Kagoshima
(Primary: On-site, Secondary: Online)
Relationship between Image Quality and Learning Effect in Color Laparoscopic Images Generation by Generative Adversarial Networks
Norifumi Kawabata (Hokkaido Univ.), Toshiya Nakaguchi (Chiba Univ.) MI2021-59
(To be available after the conference date) [more] MI2021-59
pp.59-64
IBISML 2022-01-17
10:40
Online Online Automatic Makeup Transfer with GANs and Its Quantitative Evaluation
Cuilin Wang, Jun'ichi Takeuchi (Kyushu Univ.) IBISML2021-20
Transferring makeup from a reference image with makeup to a source image without makeup has a wide range of application ... [more] IBISML2021-20
pp.17-22
IPSJ-AVM, CS, IE, ITE-BCT [detail] 2021-11-25
10:25
Online Online wganBCS: Block-wise image compressive sensing and reconstruction model using adversarial training to eliminate block effects
Boyan Chen (Hosei Univ./NPU), Kaoru Uchida (Hosei Univ.) CS2021-60 IE2021-19
The famous block-wise compressive sensing (BCS) paradigm can greatly reduce the memory consumption of sensing
matrix co... [more]
CS2021-60 IE2021-19
pp.1-6
MI, MICT [detail] 2021-11-05
15:50
Online Online [Short Paper] Sketch-based CT image generation of lung cancers using Pix2pix -- An attempt to improve representation by adopting Style Blocks --
Ryo Toda, Atsushi Teramoto (FHU), Masakazu Tsujimoto (FHUH), Hiroshi Toyama, Masashi Kondo, Kazuyoshi Imaizumi, Kuniaki Saito (FHU), Hiroshi Fujita (Gifu Univ.) MICT2021-42 MI2021-40
Generative adversarial networks (GAN) have been used to overcome the lack of data in medical images. However, such appli... [more] MICT2021-42 MI2021-40
pp.66-67
CAS, NLP 2021-10-14
15:50
Online Online Implementation of a Generative Adversarial Network as Bitwise Neural Network
Takuma Matsuno, Gauthier Lovic (Ariake College) CAS2021-28 NLP2021-26
Generative Adversarial Network (GAN) is an artificial intelligence algorithm in which a generative network, which produc... [more] CAS2021-28 NLP2021-26
pp.62-67
PRMU 2021-10-09
09:00
Online Online Omni-Directional Image Representation in GAN-based Image Generator
Keisuke Okubo, Takao Yamanaka (Sophia Univ.) PRMU2021-17
The omni-directional image generation from a snapshot image taken by an ordinary camera has been developed using conditi... [more] PRMU2021-17
pp.5-10
CCS 2021-03-29
16:05
Online Online IMAS-GAN: Unsupervised Domain Translation without Cycle Consistency
Masashi Okada, Hidehiro Nakano, Arata Miyauchi (Tokyo City Univ.) CCS2020-28
CycleGAN realizes the translation between domains without using pair data. However, the configuration of two GANs and th... [more] CCS2020-28
pp.42-47
MI 2021-03-17
11:00
Online Online Optimal Design and Quality Assessment of Color Laparoscopic Super-Resolution Image by Generative Adversarial Networks
Norifumi Kawabata (Tokyo Univ. of Science), Toshiya Nakaguchi (Chiba Univ.) MI2020-91
The Generative Adversarial Networks (GAN) is unsupervised learning enabled to transform according to data characteristic... [more] MI2020-91
pp.186-190
MI 2021-03-17
13:45
Online Online Medical Image Style Translation by Adversarial Training with Paired Inputs
Kazuki Fujioka (Kobe Univ.), Takashi Matsubara (Osaka Univ.), Kuniaki Uehara (Osaka Gakuin Univ.) MI2020-96
Medical image diagnosis by artificial intelligence requires a large amount of data for learning. However, preparing such... [more] MI2020-96
pp.212-217
EMM 2021-03-04
14:45
Online Online [Poster Presentation] Improvement of Video Forgery Detection Using Generative Adversarial Networks
Yutaro Osako (Osaka Univ.), Kazuhiro Kono (Kansai Univ.), Noboru Babaguchi (Osaka Univ.) EMM2020-72
Our work aims to detect tampered objects in the spatial domain of videos with high accuracy. We target videos, including... [more] EMM2020-72
pp.28-33
IE 2021-01-21
14:45
Online Online [Invited Talk] GAN-based Image Coding Methods for Maximizing Subjective Image Quality
Shinobu Kudo (NTT) IE2020-37
The increasing image resolution and the spread of IoT devices require more efficient video storage and transmission syst... [more] IE2020-37
pp.9-13
MBE, NC
(Joint)
2020-12-18
14:50
Online Online Super resolution for sea surface temperature with CNN and GAN
Tomoki Izumi, Motoki Amagasaki, Kei Ishida, Masato Kiyama (Kumamoto Univ.) NC2020-28
In this paper, we use the deep neural networks (DNN)-based single image super-resolution (SISR) method for the super res... [more] NC2020-28
pp.1-6
PRMU 2020-09-02
15:45
Online Online Collaborative learning for generative adversarial networks
Takuya Tsukahara, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi (Chubu Univ.) PRMU2020-14
Generative adversarial networks (GANs) adversarially trains generative and discriminative models. And this is how to gen... [more] PRMU2020-14
pp.41-46
PRMU, IPSJ-CVIM 2020-05-14
13:00
Online Online Separater for Generative Adversarial Networks
Takeshi Oba, Jun Rokui (Univ of Shizuoka) PRMU2020-1
We focus on the movement between the data of the generator and the Discriminator in the Genera- tive Adversarial Network... [more] PRMU2020-1
pp.1-6
CCS 2020-03-26
11:00
Tokyo Hosei Univ. Ichigaya Campus
(Cancelled but technical report was issued)
Generative Adversarial Networks Handling Multiple Distances between Probability Distributions
Shinya Hidai, Hidehiro Nakano, Arata Miyauchi (Tokyo City Univ.) CCS2019-39
Generative Adversarial Networks (GAN) are trained by alternately training two networks. Discriminator estimates the dist... [more] CCS2019-39
pp.21-24
EMM 2020-03-05
16:45
Okinawa
(Cancelled but technical report was issued)
[Poster Presentation] Video Forgery Detection Using Generative Adversarial Networks
Shoken Ohshiro (Osaka Univ.), Kazuhiro Kono (Kansai Univ.), Noboru Babaguchi (Osaka Univ.) EMM2019-122
The purpose of our work is to detect the regions of tampered objects in the spatial domain of videos by passive approach... [more] EMM2019-122
pp.107-112
NC, MBE
(Joint)
2020-03-05
10:45
Tokyo University of Electro Communications
(Cancelled but technical report was issued)
YuruGAN: Yuru-Charas Generated by Generative Adversarial Networks
Yuki Hagiwara, Toshihisa Tanaka (TUAT) NC2019-93
Yuru-chara is a mascot character created by local governments and companies for the purpose of publicizing information o... [more] NC2019-93
pp.101-106
ITE-HI, IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] 2020-02-28
15:10
Hokkaido Hokkaido Univ.
(Cancelled but technical report was issued)
Unpaired Learning for Noise-free, Scale Invariant, and Interpretable Image Enhancement
Satoshi Kosugi, Toshihiko Yamasaki (Univ. of Tokyo) ITS2019-52 IE2019-90
This paper tackles unpaired image enhancement, a task of learning a mapping function which transforms input images into ... [more] ITS2019-52 IE2019-90
pp.311-316
MI 2020-01-29
13:50
Okinawa OKINAWAKEN SEINENKAIKAN Investigation of Hard Exudates Image Generated by Cramer Generative Adversarial Networks
Maho Fujita, Yuji Hatanaka, Wataru Sunayama (Univ. of Shiga Prefecture), Chisako Muramatsu (Shiga Univ.), Hiroshi FUjita (Gifu Univ.) MI2019-85
(To be available after the conference date) [more] MI2019-85
pp.85-89
IMQ, HIP 2019-07-19
16:20
Hokkaido Satellite Campus, Sapporo City University A Note on Semantic Evaluation of Images Generated by Text-to-image Generative Adversarial Networks
Rintaro Yanagi, Togo Ren, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) IMQ2019-5 HIP2019-33
Evaluating the quality of generated images from input sentences is important to verify the effectiveness of text-to-imag... [more] IMQ2019-5 HIP2019-33
pp.21-24
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