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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 23  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
IN, CCS
(Joint)
2022-08-05
09:40
Hokkaido Hokkaido University(Centennial Hall)
(Primary: On-site, Secondary: Online)
Machine Learning-Based Network Traffic Prediction with Tunable Parameters
Kaito Kuriyama, Kohei Watabe (Nagaoka Univ. of Tech.) IN2022-20
Network evaluation has become increasingly important in recent years.
Network evaluation requires large amounts of traf... [more]
IN2022-20
pp.27-32
NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] 2022-06-28
10:05
Okinawa
(Primary: On-site, Secondary: Online)
Learning Attribute Vector Fields in GAN Latent Space
Takehiro Aoshima, Takashi Matsubara (Osaka Univ.) NC2022-12 IBISML2022-12
Generative Adversarial Networks (GANs) can generate a great variety of high-quality images.
Despite their ability to g... [more]
NC2022-12 IBISML2022-12
pp.94-99
SP, IPSJ-MUS, IPSJ-SLP [detail] 2022-06-17
15:00
Online Online SP2022-13 We investigate the method for unsupervised learning of artifacts correction networks used for post-processing of Multi B... [more] SP2022-13
pp.49-54
MI 2022-01-26
13:00
Online 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
Improving of personal computer performance, it is possible for healthcare workers and related researchers to support for... [more] MI2021-59
pp.59-64
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
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
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
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
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
PRMU, BioX 2019-03-18
10:15
Tokyo   Talking Head Generation with Deep Phoneme and Viseme Representation and Generative Adversarial Networks
Takaaki Yasui, Yuta Nakashima, Noboru Babaguchi (Osaka Univ.) BioX2018-53 PRMU2018-157
In this paper, we propose to generate talking head given an audio input.Some existing methods generate photorealistic ta... [more] BioX2018-53 PRMU2018-157
pp.143-148
PN, EMT, OPE, EST, MWP, LQE, IEE-EMT [detail] 2019-01-18
14:45
Osaka Osaka University Nakanoshima Center Super-resolution for GPR Images by Deep Learning Using Generative Adversarial Networks
Jun Sonoda (NIT, Sendai), Tomoyuki Kimoto (NIT, Oita) PN2018-75 EMT2018-109 OPE2018-184 LQE2018-194 EST2018-122 MWP2018-93
Recently, deterioration of social infrastructures such as tunnels and bridges become a serious social problem. It is req... [more] PN2018-75 EMT2018-109 OPE2018-184 LQE2018-194 EST2018-122 MWP2018-93
pp.237-242
EST, MW, EMCJ, IEE-EMC [detail] 2018-10-19
13:35
Aomori Hachinohe Chamber of Commerce and Industry(Hachinohe city, Aomori) Underground Model Inversion from GPR Images by Deep Learning Using Generative Adversarial Networks
Jun Sonoda (NIT, Sendai), Tomoyuki Kimoto (NIT, Oita) EMCJ2018-53 MW2018-89 EST2018-75
Recently, deterioration of social infrastructures such as tunnels and bridges become a serious social problem. It is req... [more] EMCJ2018-53 MW2018-89 EST2018-75
pp.115-119
SANE 2018-10-12
13:30
Tokyo The University of Electro-Communications Automatic Target Recognition based on Generative Adversarial Networks for Synthetic Aperture Radar Images
Yang-Lang Chang, Bo-Yao Chen, Chih-Yuan Chu, Sina Hadipour (NTUT), Hirokazu Kobayashi (OIT) SANE2018-51
Synthetic Aperture Radar (SAR) is an all day and all weather condition imaging technique which is widely used in nationa... [more] SANE2018-51
pp.41-44
EST 2018-09-06
16:05
Okinawa Kumejima-machi, Okinawa Clutter Reduction from GPR Image by Deep Learning Using Generative Adversarial Network
Jun Sonoda (NIT, Sendai College), Tomoyuki Kimoto (NIT, Oita College) EST2018-51
Recently, deterioration of social infrastructures such as tunnels and bridges become a serious social problem. It is req... [more] EST2018-51
pp.47-51
ICSS, IA 2018-06-26
11:40
Ehime Ehime University A Study on Extraction Method of Characteristics of Malware Using Generative Adversalial Networks
Keisuke Furumoto, Ryoichi Isawa, Takeshi Takahashi, Daisuke Inoue (NICT) IA2018-13 ICSS2018-13
To classify malware families including many subspecies, several methods have been proposed for acquiring malware feature... [more] IA2018-13 ICSS2018-13
pp.77-82
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