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 |