Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
PRMU, IBISML, IPSJ-CVIM [detail] |
2023-03-02 11:05 |
Hokkaido |
Future University Hakodate (Primary: On-site, Secondary: Online) |
On the Effectiveness of Formula-Driven Supervised Learning for Medical Image Tasks Ryuto Endo, Shuya Takahashi, Eisaku Maeda (TDU) PRMU2022-71 IBISML2022-78 |
Deep learning for image information processing often uses manually maintained natural image data. However, these data ha... [more] |
PRMU2022-71 IBISML2022-78 pp.71-75 |
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 |
SP, IPSJ-SLP, EA, SIP [detail] |
2023-02-28 15:55 |
Okinawa |
(Primary: On-site, Secondary: Online) |
Self-Supervised Learning With Spatial Audio-Visual Recording for Sound Event Localization and Detection Yoto Fujita (Kyoto Univ.), Yoshiaki Bando (AIST), Keisuke Imoto (Doshisha Univ./AIST), Masaki Onihsi (AIST), Yoshii Kazuyoshi (Kyoto Univ.) EA2022-89 SIP2022-133 SP2022-53 |
This paper describes an unsupervised pre-training method for sound event localization and detection (SELD) on multi-chan... [more] |
EA2022-89 SIP2022-133 SP2022-53 pp.78-82 |
SP, IPSJ-SLP, EA, SIP [detail] |
2023-02-28 17:15 |
Okinawa |
(Primary: On-site, Secondary: Online) |
DNN-based Noise Reduction Using Noise Signal for Target Signal Ryota Hiromasa, Hien Ohnaka, Ryoichi Miyazaki (NITTC) EA2022-93 SIP2022-137 SP2022-57 |
This study proposes a DNN-based noise reduction method that uses noise signals instead of clean speech signals as the ta... [more] |
EA2022-93 SIP2022-137 SP2022-57 pp.101-106 |
SP, IPSJ-SLP, EA, SIP [detail] |
2023-03-01 09:50 |
Okinawa |
(Primary: On-site, Secondary: Online) |
Domain Adaptation for Improving End-to-end ASR Performance of Classroom Speech with Variable Recording Condition Raufun Nahar, Rino Suzuki, Atsuhiko Kai (Shizuoka Univ.) EA2022-101 SIP2022-145 SP2022-65 |
Automatic speech recognition (ASR) of real-world speech recorded in real environment has been a challenge in the field o... [more] |
EA2022-101 SIP2022-145 SP2022-65 pp.153-158 |
SP, IPSJ-SLP, EA, SIP [detail] |
2023-03-01 11:00 |
Okinawa |
(Primary: On-site, Secondary: Online) |
Analysis of Noisy-target Training for DNN-based speech enhancement and investigation towards its practical use Takuya Fujimura, Tomoki Toda (Nagoya Univ.) EA2022-112 SIP2022-156 SP2022-76 |
Deep neural network (DNN)-based speech enhancement usually uses a clean speech as a training target. However, it is hard... [more] |
EA2022-112 SIP2022-156 SP2022-76 pp.221-226 |
SP, IPSJ-SLP, EA, SIP [detail] |
2023-03-01 15:50 |
Okinawa |
(Primary: On-site, Secondary: Online) |
The linguistic influence on speaker verification based on Self-Supervised Learning Tomoka Wakamatsu (Tokyo Metropolitan Univ.), Atsushi Ando (NTT), Sayaka Shiota (Tokyo Metropolitan Univ.), Ryo Masumura (NTT), Hitoshi Kiya (Tokyo Metropolitan Univ.) EA2022-118 SIP2022-162 SP2022-82 |
In recent years, statistical models utilizing Self-Supervised Learning (SSL) have been employed in various fields
It ha... [more] |
EA2022-118 SIP2022-162 SP2022-82 pp.247-252 |
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 |
IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] |
2023-02-22 10:00 |
Hokkaido |
Hokkaido Univ. |
ITS2022-60 IE2022-77 |
Unsupervised domain adaptation (UDA) is extremely effective for transferring knowledge from a label-rich source domain t... [more] |
ITS2022-60 IE2022-77 pp.101-106 |
IBISML |
2022-12-22 15:30 |
Kyoto |
Kyoto University (Primary: On-site, Secondary: Online) |
[Short Paper]
Semi supervised image classification using unreliable pseudo label Jihong Hu, Yinhao Li, Yen-Wei Chen (Ritsumeikan Univ.) IBISML2022-47 |
Semi-supervised learning (SSL), which automatically annotates unlabeled data with pseudo labels during training, has ach... [more] |
IBISML2022-47 pp.24-29 |
IBISML |
2022-12-23 11:10 |
Kyoto |
Kyoto University (Primary: On-site, Secondary: Online) |
Enhancement of Audio Signals Using Learning from Positive and Unlabelled Data Nobutaka Ito, Masashi Sugiyama (UTokyo) IBISML2022-56 |
Audio signal enhancement (SE) is the task of extracting a desired class of sounds (a “signal”) from an observed sound mi... [more] |
IBISML2022-56 pp.94-100 |
RCS, NS (Joint) |
2022-12-16 10:45 |
Aichi |
Nagoya Institute of Technology, and Online (Primary: On-site, Secondary: Online) |
[Encouragement Talk]
A study on CSI-based Human Detection System Using Semi-Supervised Machine Learning Naoki Osumi, Kosuke Tsuji, Ryotaro Isshiki, Yuhei Nagao, Leonardo Lanante, Masayuki Kurosaki, Hiroshi Ochi (Kyutech) RCS2022-201 |
In recent years, research on CSI (Channel State Information) based wireless sensing using wireless LAN has been gatherin... [more] |
RCS2022-201 pp.87-92 |
PRMU |
2022-12-15 15:30 |
Toyama |
Toyama International Conference Center (Primary: On-site, Secondary: Online) |
Training Method for Image-based Instance Segmentation by Video-based Object-Centric Representation Learning Tomokazu Kaneko, Ryosuke Sakai, Soma Shiraishi (NEC) PRMU2022-40 |
Object-centric representation learning (OCRL) aims to separate and extract object-wise representations from an image.
... [more] |
PRMU2022-40 pp.43-48 |
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 |
NLC, IPSJ-NL, SP, IPSJ-SLP [detail] |
2022-12-01 15:20 |
Tokyo |
(Primary: On-site, Secondary: Online) |
Domain and language adaptation of large-scale pretrained model for speech recognition of low-resource language Kak Soky (Kyoto University), Sheng Li (NICT), Chenhui Chu, Tatsuya Kawahara (Kyoto University) NLC2022-17 SP2022-37 |
The self-supervised learning (SSL) models are effective for automatic speech recognition (ASR). Due to the huge paramete... [more] |
NLC2022-17 SP2022-37 pp.45-49 |
SWIM |
2022-11-26 15:40 |
Tokyo |
Kikai-Shinko-Kaikan Bldg. (Primary: On-site, Secondary: Online) |
Sample Program Recommendation System to Support Programming Education Yoshihisa Udagawa (Tokyo Univ. of Information Sciences) SWIM2022-24 |
One effective way to learn programming techniques is to reuse sample programs. As the number of sample programs increase... [more] |
SWIM2022-24 pp.20-26 |
CAS, MSS, IPSJ-AL [detail] |
2022-11-18 14:05 |
Kochi |
(Primary: On-site, Secondary: Online) |
Unsupervised Representation Learning over Decentralized Federated Learning Haruki Sakurai, Hideya Ochiai, Hiroshi Esaki (Univ. Tokyo) CAS2022-54 MSS2022-37 |
Contrastive Learning is a form of self-supervised learning, a method for learning a general-purpose encoder using a larg... [more] |
CAS2022-54 MSS2022-37 pp.79-82 |
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 |
MVE |
2022-09-09 10:30 |
Tokyo |
(Primary: On-site, Secondary: Online) |
Self-Supervised Learning for Echo Chamber-aware Friend Recommendation Luwei Zhang, Toshihiko Yamsaski (UTokyo) MVE2022-14 |
In recommender systems, the creation of echo chambers and filter bubbles obviously lowers the diversity of recommendatio... [more] |
MVE2022-14 pp.22-25 |
SIP |
2022-08-25 14:33 |
Okinawa |
Nobumoto Ohama Memorial Hall (Ishigaki Island) (Primary: On-site, Secondary: Online) |
Structured Deep Image Prior with Interscale Thresholding Jikai Li, Shogo Muramatsu (Niigata Univ.) SIP2022-55 |
This work proposes a novel image denoising technique inspired by the deep image prior (DIP) method. Our contribution is ... [more] |
SIP2022-55 pp.31-36 |