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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 85  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
MI 2024-03-03
17:18
Okinawa OKINAWAKEN SEINENKAIKAN
(Primary: On-site, Secondary: Online)
3D shape reconstruction of colon with model-based unsupervised depth estimation
Natsu Onozaka (Nagoya Univ.), Hayato Itoh (Fukuoka Univ.), Masahiro Oda (Nagoya Univ.), Masashi Misawa (Showa Univ.), Yuichi Mori (UiO), Shin-ei Kudo (Showa Univ.), Kensaku Mori (Nagoya Univ.) MI2023-60
We propose unsupervised trainig for the pose estimation in 3D reconstrcution of the colon from colonoscopic images by cl... [more] MI2023-60
pp.87-90
MI 2024-03-04
09:00
Okinawa OKINAWAKEN SEINENKAIKAN
(Primary: On-site, Secondary: Online)
Distance-informed adversarial learning for metal artifact reduction
Daisuke Shigemori, Megumi Nakao (Kyoto Univ.) MI2023-62
In this study, we propose an adversarial learning framework that utilises distance information from metal to reduce CT m... [more] MI2023-62
pp.95-98
NS, IN
(Joint)
2024-02-29
11:10
Okinawa Okinawa Convention Center An unsupervised online learning-based traffic classification and anomaly detection method for 5G-IIoT systems
Yuxuan Shi, Qianqian Pan, Akihiro Nakao (U Tokyo) NS2023-188
In the context of Society 5.0, the evolution of the Internet of Things (IoT) and its ever growing demands of massive Mac... [more] NS2023-188
pp.96-102
DE, IPSJ-DBS 2023-12-26
14:00
Tokyo Institute of Industrial Science, The University of Tokyo Interpretation of unsupervised clustering based on XAI
Yu Sasaki, Fumiaki Saitoh (CIT) DE2023-28
Explainable Artificial Intelligence (XAI) aims to introduce transparency and interpretability into the decision-making o... [more] DE2023-28
pp.1-6
WIT, HI-SIGACI 2023-12-07
11:15
Tokyo AIST Tokyo Waterfront (TBD) On Modeling Daily Activities of the Elderly Living Alone Using Markov Series of Dirichlet Multinomial Mixture Models
Ken Sadohara (AIST) WIT2023-30
To develop smart home technology designed to analyze the activity of residents based on the logs of installed sensors, a... [more] WIT2023-30
pp.31-36
IA 2023-09-22
10:40
Hokkaido Hokkaido Univeristy
(Primary: On-site, Secondary: Online)
OLIViS: An OSINT-Based Lightweight Method for Identifying Video Content Services for Capacity Planning in Backbone ISPs
Yuki Tamura, Fumio Teraoka, Takao Kondo (Keio Univ.) IA2023-23
As of 2022, 66% of Internet traffic is generated by video content services, among which Netflix and YouTube are the domi... [more] IA2023-23
pp.75-82
PRMU, IPSJ-CVIM 2023-05-19
15:40
Aichi
(Primary: On-site, Secondary: Online)
Object-Centric Representation Learning with Attention Mechanism
Hidemoto Nakada, Hideki Asoh (AIST) PRMU2023-13
For object-centric representation learning, several slot-based methods, that separate objects using masks and learn the ... [more] PRMU2023-13
pp.68-73
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
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
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
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
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
SAT, RCS
(Joint)
2022-08-26
11:40
Hokkaido
(Primary: On-site, Secondary: Online)
Inter-cell Interference Control by Joint Transmit Power and Transmit Beamforming Control based on Machine Learning
Naoto Tamada, Yuyuan Chang, Kazuhiko Fukawa (Tokyo Tech) RCS2022-118
In mobile communications, densely deployed small cell systems using the same frequency band are expected to increase the... [more] RCS2022-118
pp.120-125
AI 2022-07-04
16:00
Hokkaido
(Primary: On-site, Secondary: Online)
Unsupervised Learning of a Dynamic Task Ordering Model for Crowdsourcing
Ryo Yanagisawa (Waseda Univ.), Susumu Saito, Teppei Nakano (ifLab Inc.), Tetsunori Kobayashi, Tetsuji Ogawa (Waseda Univ.) AI2022-14
An unsupervised learning method for a dynamic task ordering model that optimizes the number of orders according to the d... [more] AI2022-14
pp.72-76
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
SP, IPSJ-MUS, IPSJ-SLP [detail] 2022-06-18
15:00
Online Online Unsupervised Training of Sequential Neural Beamformer Using Blindly-separated and Non-separated Signals
Kohei Saijo, Tetsuji Ogawa (Waseda Univ.) SP2022-25
We present an unsupervised training method of the sequential neural beamformer (Seq-NBF) using the separated signals fro... [more] SP2022-25
pp.110-115
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
SeMI, IPSJ-DPS, IPSJ-MBL, IPSJ-ITS 2022-05-26
13:45
Okinawa
(Primary: On-site, Secondary: Online)
Unsupervised Learning-based Non-invasive Fetal ECG Signal Quality Assessment
Xintong Shi, Kohei Yamamoto, Tomoaki Ohtsuki (Keio Univ.), Yutaka Matsui, Kazunari Owada (Atom Medical Co., Ltd.) SeMI2022-4
For fetal heart rate (FHR) monitoring, the non-invasive fetal electrocardiogram (FECG) obtained from abdomen surface ele... [more] SeMI2022-4
pp.15-19
MW 2022-03-04
11:10
Online Online Deep-Learning Based Anomaly Detection Method for Microwave Non-destructive Road Monitoring
Takahide Morooka, Shouhei Kidera (Univ. of Electro-Communications) MW2021-134
Microwave radar is promising as large-scale and speedy non-destructive monitoring tool for aging road or tunnel because ... [more] MW2021-134
pp.128-133
IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] 2022-02-21
11:30
Online Online ITS2021-28 IE2021-37 The dynamic range of electronic imaging is orders of magnitudes smaller than that of human vision. To obtain images of h... [more] ITS2021-28 IE2021-37
pp.19-24
 Results 1 - 20 of 85  /  [Next]  
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