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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| RCC, ISEC, IT, WBS | 2024-03-14 10:20 |
Osaka | Osaka Univ. (Suita Campus) | Improving classification accuracy of imaged malware through data expansion Kaoru Yokobori, Hiroki Tanioka, Masahiko Sano, Kenji Matsuura, Tetsushi Ueta (Tokushima Univ.)  IT2023-115 ISEC2023-114 WBS2023-103 RCC2023-97 |
Although malware-based attacks have existed for years, malware infections increased in 2019 and 2020. One of the reaso... [more] |
IT2023-115 ISEC2023-114 WBS2023-103 RCC2023-97 pp.259-264 |
| PRMU, IBISML, IPSJ-CVIM | 2024-03-04 10:52 |
Hiroshima | Hiroshima Univ. Higashi-Hiroshima campus (Primary: On-site, Secondary: Online) |
Illust Protection against Generative AI using DnCNN Yukiya Fukuda, Daiju Kanaoka (Kyutech), Hakaru Tamukoh (Kyutech/Research Center for Neuromorphic AI Hardware) PRMU2023-71 |
Although generative AI such as stable diffusion are rapidly developing, but there are concerns about problems such as un... [more] | PRMU2023-71 pp.116-121 |
| ICTSSL, CAS | 2024-01-25 11:15 |
Kanagawa | (Primary: On-site, Secondary: Online) |
AI development using both GAN and CycleGAN Yoshiaki Shinjo, Hideaki Okazaki (SIT) CAS2023-86 ICTSSL2023-39 |
In this report, I describe a method for constructing an image generation AI by deep learning using CycleGAN in combinati... [more] | CAS2023-86 ICTSSL2023-39 pp.23-25 |
| TL | 2023-12-23 17:30 |
Online | National Tsing Hua University（Taiwan） (Primary: Online, Secondary: On-site) |
Possible Improvements of Learner Essays through Effective Use of Assistive Writing Tools
-- Subjective Expert Evaluation of System Proposals for Revision -- Yasunari Harada (Waseda U.), Miwa Morishita (Kobe Gakuin U.), Kevin Tuan, Jason S. Chang (NTHU) TL2023-38 |
Development and wide-spread use of native and learner corpora in these two decades, tother with recent new and accelerat... [more] | TL2023-38 pp.41-46 |
| BioX | 2023-10-13 10:20 |
Okinawa | Nobumoto Ohama Memorial Hall | Discrimination between Real and Generated Gestures of Speakers
-- An Attempt to Improve Generalization Performance in Unseen Generation Methods through Self-Supervised Learning -- Geng Mu (AGU), Naoshi Kaneko (TDU), Kazuhiko Sumi (AGU) BioX2023-67 |
Currently, discerning artificially generated misinformation is a critical societal challenge, with research progressing ... [more] | BioX2023-67 pp.44-49 |
| CQ, MIKA (Joint) |
2023-08-31 15:55 |
Fukushima | Tenjin-Misaki Sports Park | Semantic Communication with Masked Autoencoders: Enhancing Efficiency in Image Transmission Jiale Wu, Zhaoyang Du, Celimuge Wu, Tsutomu Yoshinaga (UEC) CQ2023-29 |
Semantic communication, a promising candidate for 6G technology, has become a research hot spot. However, existing studi... [more] | CQ2023-29 pp.20-25 |
| PRMU, IPSJ-CVIM | 2023-05-18 14:30 |
Aichi | (Primary: On-site, Secondary: Online) |
Discriminating between fake and real gestures in automatic gesture generation Geng Mu, Nosh Kaneko, Kazuhiko Sumi (AGU) PRMU2023-5 |
In recent years, gestures play a crucial role in communication with anthropomorphized agents and robots. The use of gest... [more] | PRMU2023-5 pp.22-26 |
| CCS | 2023-03-27 09:20 |
Hokkaido | RUSUTSU RESORT | Learning Commutative Vector Field in Latent Space of Deep Generative Model Takehiro Aoshima, Takashi Matsubara (Osaka Univ.) CCS2022-83 |
Deep generative models, such as generative adversarial networks (GANs), are known for generating high-quality images. Ho... [more] | CCS2022-83 pp.113-116 |
| NC, MBE (Joint) |
2023-03-14 16:15 |
Tokyo | The Univ. of Electro-Communications (Primary: On-site, Secondary: Online) |
Generation and inpainting of Kuzushiji image data using Boltzmann Machines Hiroki Ikoma (NAIST), Mauricio Bermudez, Minho Lee (KNU), Kazushi Ikeda (NAIST) NC2022-108 |
It is almost impossible for the average person to read Kuzushiji today. For this reason, there is a need to develop tool... [more] | NC2022-108 pp.94-98 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2023-03-02 09:00 |
Hokkaido | Future University Hakodate (Primary: On-site, Secondary: Online) |
Toward Regularizing Neural Networks with Meta-Learning Generative Models Shin'ya Yamaguchi (NTT/Kyoto Univ.), Daiki Chijiwa, Sekitoshi Kanai, Atsutoshi Kumagai (NTT), Hisashi Kashima (Kyoto Univ.) PRMU2022-58 IBISML2022-65 |
This paper investigates methods for improving generative data augmentation for deep learning. Generative data augmentati... [more] | PRMU2022-58 IBISML2022-65 pp.1-6 |
| 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 |
|
| CCS | 2022-03-27 15:40 |
Hokkaido | RUSUTSU RESORT HOTEL & CONVENTION (Primary: On-site, Secondary: Online) |
Evaluation of Industrial Anomaly Detection using Diffusion Model Yu Kashihara, Takashi Matsubara (Osaka Univ.) CCS2021-48 |
Anomaly detection by generative models is achieved by comparing the reconstruction and the original image. However, exis... [more] | CCS2021-48 pp.72-77 |
| 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 16:45 |
Online | Online | A Note on Disentanglement Using Deep Generative Model Based on Variational Autoencoder
-- Introduction of Regularization Losses Based on Metrics of Disentangled Representation -- Nao Nakagawa, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
In this paper, we study disentangled representation learning using a deep generative model based on Variational Autoenco... [more] | |
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