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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 66  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
PRMU, IBISML, IPSJ-CVIM [detail] 2023-03-02
Hokkaido Future University Hakodate
(Primary: On-site, Secondary: Online)
A Semi-Supervised Learning Framework using Mixed Augmentations and Scheduled Pseudo-Label Loss for Handwritten Text Recognition
Masayuki Honda, Hung Tuan Nguyen, Cuong Tuan Nguyen (TUAT), Cong Kha Nguyen, Ryosuke Odate, Takashi Kanemaru (Hitachi Ltd.), Masaki Nakagawa (TUAT)
IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] 2023-02-21
Hokkaido Hokkaido Univ. Improving Fashion Compatibility Prediction with Color Distortion Prediction
Ling Xiao, Toshihiko Yamasaki (UTokyo)
NLC, IPSJ-NL, SP, IPSJ-SLP [detail] 2022-11-30
(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
SIS, ITE-BCT 2022-10-14
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
CCS, NLP 2022-06-09
(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
SIS, IPSJ-AVM 2022-06-09
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
MI 2022-01-26
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
SeMI 2022-01-20
(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
CS 2021-10-15
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
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
EA, US, SP, SIP, IPSJ-SLP [detail] 2021-03-04
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
IBISML 2020-10-20
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
PRMU, IPSJ-CVIM 2020-03-17
(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]
EST 2020-01-30
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
SeMI 2020-01-31
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
SR 2019-12-05
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
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
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]
IA 2019-09-05
Hokkaido Hokkaido Univ. Humanities and Social Sciences Classroom Bldg, W102 A Study on Estimating Communication Delays using Graph Convolutional Networks with Semi-Supervised Learning
Taisei Suzuki, Yuichi Yasuda, Ryo Nakamura, Hiroyuki Ohsaki (Kwansei Gakuin Univ.) IA2019-10
In large-scale communication networks consisting of many end hosts and routers, accurate acquisition, measurement, and e... [more] IA2019-10
Osaka I-Site Nanba(Osaka) Deep learning-based classification for the automatic of eNodeB state management in LTE networks
Kazuki Hara (Tsukuba Univ.), Kohei Shiomoto (TCU), Chin Lam Eng, Sebastian Backstad (Ericsson Japan) RCC2019-41 NS2019-77 RCS2019-134 SR2019-53 SeMI2019-50
It is crucial to identify the cause immediately when a failure occurs at base station of mobile communication. However, ... [more] RCC2019-41 NS2019-77 RCS2019-134 SR2019-53 SeMI2019-50
pp.145-150(RCC), pp.171-176(NS), pp.167-172(RCS), pp.177-182(SR), pp.159-164(SeMI)
 Results 1 - 20 of 66  /  [Next]  
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