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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| ICTSSL, CAS | 2023-01-27 11:25 |
Tokyo | TBD (Primary: On-site, Secondary: Online) |
On automatic illustrations generated by GAN networks Aika Honne, Kazuya Ozawa, Hideaki Okazaki (SIT)  CAS2022-83 ICTSSL2022-47 |
In this report, we discuss automatic illustrations generated by GAN networks (adversarial generative networks). First, a... [more] | CAS2022-83 ICTSSL2022-47 pp.104-107 |
| PRMU | 2022-12-16 14:40 |
Toyama | Toyama International Conference Center (Primary: On-site, Secondary: Online) |
Data Augmentation Shumpei Takezaki (Kyushu Univ.), Kiyohito Tanaka (Kyoto Second Red Cross Hospital), Seiichi Uchida, Takeaki Kadota (Kyushu Univ.) PRMU2022-50 |
Disease severity regression by a convolutional neural network (CNN) for medical images requires a sufficient number of i... [more] | PRMU2022-50 pp.95-99 |
| SRW, SeMI, CNR (Joint) |
2022-11-25 11:05 |
Tochigi | Epinard Nasu (Primary: On-site, Secondary: Online) |
n/a Natsuki Ikuo, Sorachi Kato, Hiroaki Shinmiya, Takuya Fujihashi (Osaka Univ.), Tomoki Murakami (NTT), Takashi Watanabe, Shunsuke Saruwatari (Osaka Univ.) SeMI2022-58 |
(To be available after the conference date) [more] | SeMI2022-58 pp.31-36 |
| 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 |
| MI | 2022-07-08 17:00 |
Hokkaido | (Primary: On-site, Secondary: Online) |
[Short Paper]
Weakly-Supervised Focal Liver Lesion Detection in CT Images He Li, Yutaro Iwamoto (Ritsumeikan Univ.), Xianhua Han (Yamaguchi Univ.), Lanfen Lin, Ruofeng Tong, Hongjie Hu (Zhejiang Univ.), Akira Furukawa (Tokyo Metropolitan Univ.), Shuzo Kanasaki (Koseikai Takeda Hospital), Yen-Wei Chen (Ritsumeikan Univ.) MI2022-40 |
Convolutional neural networks have been widely used for anomaly detection and one of their most common methods is autoen... [more] | MI2022-40 pp.30-33 |
| AI | 2022-07-04 16:50 |
Hokkaido | (Primary: On-site, Secondary: Online) |
A generative model for generation of playable levels in 2D video games. Soichiro Takata, Yuichi Sei, Yasuyuki Tahara, Akihiko Ohsuga (UEC) AI2022-16 |
(To be available after the conference date) [more] | AI2022-16 pp.82-87 |
| 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 |
| SIS, IPSJ-AVM | 2022-06-09 15:00 |
Fukuoka | KIT(Wakamatsu Campus) (Primary: On-site, Secondary: Online) |
[Invited Talk]
Advanced applications of machine learning techniques towards high-performance and cost-effective visual inspection AI Terumasa Tokunaga (Kyutech) SIS2022-6 |
Visual inspection is an essential step for quality control in manufacturing. Recently, many researchers have shown great... [more] | SIS2022-6 p.30 |
| IMQ | 2022-05-27 14:25 |
Tokyo | Classification-ESRGAN
-- Synthesis of super-resolution images based on subject categorization -- Jingan Liu, Atsumu Harada, Naiwala P. Chandrasiri (Kogakuin Univ.) IMQ2022-3 |
In recent years, super-resolution techniques have been significantly developed based on deep learning. In particular, GA... [more] | IMQ2022-3 pp.12-17 |
|
| EMM | 2022-03-07 15:25 |
Online | (Primary: Online, Secondary: On-site) (Primary: Online, Secondary: On-site) |
[Poster Presentation]
A Proposal for Emotion-Expressive Editor:EmoEditor by Font Changing Yuuki Shimamura, Michiharu Niimi (KIT) EMM2021-100 |
Text media is one of important ways in communications on computers. For example, email, LINE or Twitter uses it frequent... [more] | EMM2021-100 pp.46-51 |
| CNR, BioX | 2022-03-04 14:20 |
Online | Online | Synthesizing Deep Master Voices for Wolf Attacking on Speaker Recognition Systems Jun Tsuchiya, Masakatsu Nishigaki, Tetsushi Ohki (Shizuoka Univ.) BioX2021-53 CNR2021-34 |
In this paper, we propose an attack on speaker verification systems by Deep Master Voice using GAN-based wolf voice.GAN-... [more] | BioX2021-53 CNR2021-34 pp.33-38 |
| CNR, BioX | 2022-03-04 15:50 |
Online | Online | Gait-based Age Estimation Using Angle Suppression Learning Kodai Yamano (Osaka Univ.), Daigo Muramatsu (Seikei Univ.), Noriko Takemura, Yasushi Yagi (Osaka Univ.) BioX2021-56 CNR2021-37 |
Robustness for observation view difference is an important and expected property for gait-based age estimation. In order... [more] | BioX2021-56 CNR2021-37 pp.51-56 |
| 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 |
| IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] | 2022-02-21 15:20 |
Online | Online | A Note on Electron Microscope Image Generation from Mix Proportion and Material Property via Generative Adversarial Network for Rubber Materials Rintaro Yanagi, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
Estimating the properties of rubber materials from ingredients is necessary to accelerate rubber material development. A... [more] | |
| 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 |
| 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 |
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