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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] | 2022-02-21 12:45 |
Online | Online | Regularizing Generative Adversarial Networks with Internal Representation of Generators Yusuke Hara, Toshihiko Yamasaki (UTokyo)  ITS2021-29 IE2021-38 |
In training generative adversarial networks, maintaining the criteria of the discriminator stably is crucial to training... [more] | ITS2021-29 IE2021-38 pp.25-30 |
| 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 |
| 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 |
| IBISML | 2022-01-18 13:20 |
Online | Online | IBISML2021-24 |
We aim to explain a black-box classifier with the form: `data X is classified as class Y because X has A, B and does not... [more] | IBISML2021-24 pp.45-53 |
| NLP | 2021-12-18 16:05 |
Oita | J:COM Horuto Hall OITA | Digital Nature of Language
-- Principles to give birth to Homo Sapiens -- Kumon Tokumaru (Writer) NLP2021-68 |
“Human Beings” are called as “Man with Wisdom (Homo Sapiens)”. However, the syllable vocalizing ability which human bein... [more] | NLP2021-68 pp.114-119 |
| 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 |
| 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 |
| CNR | 2021-09-21 15:30 |
Online | Online | Imitation Learning: Learning Simple Tasks from a Single Demonstration using Generative Adversarial Network Tho Nguyen Duc, Chanh Minh Tran, Phan Xuan Tan, Eiji Kamioka CNR2021-6 |
Imitation learning has been successfully applied to train autonomous agents in complex tasks (e.g., self-driving, assist... [more] | CNR2021-6 pp.12-15 |
| 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 | 2021-03-15 15:15 |
Online | Online | Deep State-Space Modeling of FMRI Images with Disentangle Attributes Koki Kusano (Kobe Univ.), Takashi Matsubara (Osaka Univ.), Kuniaki Uehara (Osaka Gakuin Univ.) MI2020-59 |
As well as the disorder and other targets, nuisance attributes such as age, gender, and scanner specifications underlie ... [more] | MI2020-59 pp.56-61 |
| 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 |
| IBISML | 2021-03-02 11:15 |
Online | Online | Interdisciplinary Integration by Artificial Intelligence
-- Tasks of Discipline Science -- Kumon Tokumaru (Writer) IBISML2020-37 |
It is time to integrate interdisciplinary sciences to develop Collective Human Intelligence. Research results of discipl... [more] | IBISML2020-37 pp.24-29 |
| 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 | 2020-12-18 14:25 |
Online | Online | Zero-shot generative model considering attribute uncertainty Yuta Sakai (Waseda Univ.), Kenta Mikawa (SIT), Masayuki Goto (Waseda Univ.) PRMU2020-59 |
Classification problems in machine learning remain an important research topic. In general, classification estimates unk... [more] | PRMU2020-59 pp.122-127 |
| IBISML | 2020-10-21 09:45 |
Online | Online | IBISML2020-18 |
A symbol emergence system is a multi-agent system where each autonomous agent forms internal representations through int... [more] | IBISML2020-18 pp.34-35 |
| MI | 2020-09-03 10:00 |
Online | Online | Lung region segmentation of thoracoscopic image with unsupervised image translation Jumpei Nitta, Megumi Nakao (Kyoto Univ.), Keiho Imanishi (e-Growth Co. Ltd.), Tetsuya Matsuda (Kyoto Univ.) MI2020-19 |
In endoscopic surgery, it is necessary to understand the three-dimensional structure of the target region to improve saf... [more] | MI2020-19 pp.13-18 |
| MI | 2020-09-03 14:25 |
Online | Online | Proposal of 3D Generative Adversarial Network for Improving Image Ouality of Cone-Beam CT Images Takumi Hase, Megumi Nakao (Kyoto Univ.), Keoho Imanishi (e-Growth Co., Ltd), Mitsuhiro Nakamura, Tetsuya Matsuda (Kyoto Univ.) MI2020-29 |
Artifacts and defects included in Cone-beam CT (CBCT) images have become an obstacle in radiation therapy and surgery su... [more] | MI2020-29 pp.51-56 |
| PRMU | 2020-09-02 11:00 |
Online | Online | Cross-Modal Realization of Logical Scientific Concepts
-- To Think with Collective Human Intelligence -- Kumon Tokumaru (Writer) PRMU2020-12 |
Words are transferred to the brain as sound stimuli, and network with individual episodic and semantic memories to gener... [more] | PRMU2020-12 pp.29-34 |
| 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 |
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