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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NC, MBE (Joint) |
2024-03-12 13:55 |
Tokyo | The Univ. of Tokyo (Primary: On-site, Secondary: Online) |
Identification of filamentous fungi by segmentation models using consistency regularization and classmix Taiga Shimizu (Yamanashi Univ.), Waleed Asghar (Oklahoma State Univ.), Ryota Kataoka, Motonobu Hattori (Yamanashi Univ.)  NC2023-57 |
In agriculture, soil diagnosis is necessary to protect the environment. However, since current diagnostic methods are no... [more] | NC2023-57 pp.81-86 |
| NLP, MSS | 2023-03-17 16:05 |
Nagasaki | (Primary: On-site, Secondary: Online) |
Lightweighting Noisy Student Semi-Supervised Learning by Applying MobileNet Yuga Morishima, Hidehiro Nakano (Tokyo City Univ.) MSS2022-108 NLP2022-153 |
Recently, Convolutional Neural Networks (CNNs) have attracted much attention in various fields such as image classificat... [more] | MSS2022-108 NLP2022-153 pp.220-224 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2023-03-02 17:00 |
Hokkaido | Future University Hakodate (Primary: On-site, Secondary: Online) |
A Semi-Supervised Learning Framework for Handwritten Text Recognition using Mixed Augmentations and Scheduled Pseudo-Label Loss Masayuki Honda, Hung Tuan Nguyen, Cuong Tuan Nguyen (TUAT), Cong Kha Nguyen, Ryosuke Odate, Takashi Kanemaru (Hitachi Ltd.), Masaki Nakagawa (TUAT) PRMU2022-97 IBISML2022-104 |
We propose Incremental Teacher Model, a semi-supervised learning (SSL) framework for handwriting text recognition. The f... [more] | PRMU2022-97 IBISML2022-104 pp.199-204 |
| IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2023-02-21 10:30 |
Hokkaido | Hokkaido Univ. | Improving Fashion Compatibility Prediction with Color Distortion Prediction Ling Xiao, Toshihiko Yamasaki (UTokyo) ITS2022-44 IE2022-61 |
Fashion compatibility prediction is suffering from the fact that the labeled dataset may become outdated quickly due to ... [more] | ITS2022-44 IE2022-61 pp.17-18 |
| NLC, IPSJ-NL, SP, IPSJ-SLP [detail] | 2022-11-30 15:30 |
Tokyo | (Primary: On-site, Secondary: Online) |
Semi-supervised joint training of text to speech and automatic speech recognition using unpaired text data Naoki Makishima, Satoshi Suzuki, Atsushi Ando, Ryo Masumura (NTT) NLC2022-14 SP2022-34 |
This paper presents a novel joint training of text to speech (TTS) and automatic speech recognition (ASR) with small amo... [more] | NLC2022-14 SP2022-34 pp.27-32 |
| SIS, ITE-BCT | 2022-10-14 10:00 |
Aomori | Hachinohe Institute of Technology (Primary: On-site, Secondary: Online) |
Robust Semi-Supervised Learning for Noisy Labels Using Early-learning Regularization and Weighted Loss Ryota Higashimoto, Soh Yoshida, Mitsuji Muneyasu (Kansai Univ.) SIS2022-16 |
Training Deep Neural Networks (DNNs) on datasets with incorrect labels (label noise) is an important challenge. In the p... [more] | SIS2022-16 pp.27-32 |
| CCS, NLP | 2022-06-09 14:15 |
Osaka | (Primary: On-site, Secondary: Online) |
Improvement of Recognition Accuracy by Sequential Execution of Unsupervised Learning and Semi-supervised Learning Hiroki Murakami, Hidehiro Nakano (Tokyo City Univ.) NLP2022-4 CCS2022-4 |
In this study, we propose a sequential learning method that improves recognition accuracy by alternately utilizing the k... [more] | NLP2022-4 CCS2022-4 pp.17-22 |
| 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 |
| MI | 2022-01-26 15:00 |
Online | Online | [Special Talk]
TBA Ryoma Bise (Kyushu Univ.) MI2021-66 |
Supervised learning (e.g., deep learning) has been used for various tasks in biomedical image analysis. While supervised... [more] | MI2021-66 p.88 |
| SeMI | 2022-01-20 15:00 |
Nagano | (Primary: On-site, Secondary: Online) |
[Short Paper]
Evaluation of Few Round Training with Distillation-Based Semi-Supervised Federated Learning Yuki Yoshida (Tokyo Tech), Sohei Itahara (Kyoto Univ.), Takayuki Nishio (Tokyo Tech) SeMI2021-65 |
This paper studies how to reduce the number of rounds in model training using Distillation-based Semi-supervised federat... [more] | SeMI2021-65 pp.48-50 |
| CS | 2021-10-15 10:40 |
Online | Online | User Data Selection using CNN Feature Extractor for Fingerprint Localization Yohei Konishi, Satoru Aikawa, Shinichiro Yamamoto, Yuta Sakai (Univ of Hyogo) CS2021-57 |
This paper scopes a method that applies CNN to Fingerprint indoor localization. AP information are used to train the CNN... [more] | CS2021-57 pp.26-31 |
| RCS, SR, NS, SeMI, RCC (Joint) |
2021-07-16 09:00 |
Online | Online | A Study on Automatic Labeling MethodUsing Semi-Supervised Learning for Wireless LAN Sensing Naoki Osumi, Kosuke Tsuji, Ryotaro Isshiki, Yuhei Nagao, Leonardo Lanante, Masayuki Kurosaki, Hiroshi Ochi (Kyutech) RCS2021-93 |
In recent years, research on CSI (Channel State Information) based wireless sensing using wireless LAN has been gatherin... [more] | RCS2021-93 pp.74-79 |
| EA, US, SP, SIP, IPSJ-SLP [detail] | 2021-03-04 09:00 |
Online | Online | Anomalous Sound Detection Using a Binary Classification Model Considering Class Centroids Ibuki Kuroyanagi, Tomiki Hayashi, Kazuya Takeda, Tomoki Toda (Nagoya Univ) EA2020-79 SIP2020-110 SP2020-44 |
In an anomalous sound detection system, it is necessary to detect unknown anomalous sounds using only normal sound data.... [more] | EA2020-79 SIP2020-110 SP2020-44 pp.114-121 |
| IBISML | 2020-10-20 10:50 |
Online | Online | System operation for estimation of road condition using tire vibration data Satoru Kawamata (Bridgestone), Tomoko Matsui (ISM), Mitsuhiro Nishida, Takeshi Masago (Bridgestone) IBISML2020-10 |
In a system that estimates the road surface condition from tire sensor data and supports safe driving, it is crucial to ... [more] | IBISML2020-10 pp.14-19 |
| PRMU, IPSJ-CVIM | 2020-03-17 10:45 |
Kyoto | (Cancelled but technical report was issued) |
Semi-Supervised Temporal Segmentation of Industrial Operation Video based on Deep Metric Learning Daiki Kawamori, Kazuaki Nakamura, Naoko Nitta, Noboru Babaguchi (Osaka Univ.) PRMU2019-92 |
Today, cameras are often installed in many production sites for various purposes. However, untrimmed raw videos captur... [more] |
PRMU2019-92 pp.139-144 |
| EST | 2020-01-30 09:40 |
Oita | Beppu International Convention Center | Embedded object identification from ground penetrating radar image by semi-supervised learning using variational auto-encoder Tomoyuki Kimoto (NIT, Oita), Jun Sonoda (NIT, Sendai) EST2019-80 |
Recently, deterioration of social infrastructures such as tunnels and bridges becomes serious social problem. It is requ... [more] | EST2019-80 pp.7-12 |
| SeMI | 2020-01-31 10:00 |
Kagawa | [Poster Presentation]
Communication-Efficient Federated Learning Using Non-Labeled Data Souhei Itahara, Takayuki Nishio, Masahiro Morikura, Koji Yamamoto (Kyoto Univ) SeMI2019-109 |
Federated learning (FL) is a machine learning setting where many mobile devices collaboratively train a machine learning... [more] | SeMI2019-109 pp.47-48 |
|
| SR | 2019-12-05 13:50 |
Okinawa | Ishigaki City Hall (Ishigaki Island) | [Poster Presentation]
Quality state analysis of eNodeB log data by semi-supervised learning using Self training Shouta Yoshida (TCU), Atsushi Morohoshi (Fujitsu Fsas), Kohei Shiomoto (TCU), Chin Lam Eng, Sebastian Backstad (Ericsson Japan) SR2019-92 |
In an LTE network where traffic is increasing year by year. It is important to quickly find the cause when a failure occ... [more] | SR2019-92 pp.29-36 |
| RISING (2nd) |
2019-11-26 14:10 |
Tokyo | Fukutake Learning Theater, Hongo Campus, Univ. Tokyo | [Poster Presentation]
A Study for Knowlage Distillation Based Semi-Supervised Federated Learning with Low Communication Cost Sohei Itahara, Takayuki Nishio, Masahiro Morikura, Koji Yamamoto (Kyoto Univ) |
Federated Learning is a decentralized learning mechanism, which enables to train machine learning (ML) model using the r... [more] | |
| NLC, IPSJ-DC | 2019-09-28 16:50 |
Tokyo | Future Corporation | Semi-supervised learning for sentiment analysis by using Triple-GAN Jincheng Yang, Rui Cao, Jing Bai, Wen Ma, Hiroyuki Shinnou (Ibaraki Univ.) NLC2019-26 |
GAN has become an effective method in the field of image,but in the field of NLP,It's difficult to design a generation ... [more] |
NLC2019-26 pp.99-102 |
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