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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 75  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
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)
(To be available after the conference date) [more]
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
IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] 2022-02-22
10:45
Online Online ITS2021-46 IE2021-55 There has been a tremendous progress in unsupervised domain adaptation (UDA), which aims to transfer knowledge acquired ... [more] ITS2021-46 IE2021-55
pp.127-132
MI 2022-01-26
13:00
Online Online Relationship between Image Quality and Learning Effect in Color Laparoscopic Images Generation by Generative Adversarial Networks
Norifumi Kawabata (Hokkaido Univ.), Toshiya Nakaguchi (Chiba Univ.) MI2021-59
Improving of personal computer performance, it is possible for healthcare workers and related researchers to support for... [more] MI2021-59
pp.59-64
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
PRMU 2021-12-16
11:00
Online Online Anomaly Detection using PatchCore with Self-attention module
Yuki Takena (Shizuoka Univ.), Yoshiki Nota, Rinpei Mochizuki (Meidensya Corp.), Itaru Matsumura (Railway Technical Research Inst.), Gosuke Ohashi (Shizuoka Univ.) PRMU2021-29
In recent years, in visual inspection of industrial products using deep learning, There are many models that achieve exc... [more] PRMU2021-29
pp.31-36
PRMU 2021-12-16
14:45
Online Online Unsupervised Logo Detection Using Adversarial Learning from Synthetic to Real Images
Rahul Kumar Jain (Ritsumeikan Univ.), Takahiro Sato, Taro Watasue, Tomohiro Nakagawa (tiwaki), Yutaro Iwamoto (Ritsumeikan Univ.), Xiang Ruan (tiwaki), Yen-Wei Chen (Ritsumeikan Univ.) PRMU2021-31
Most of the existing deep learning based logo detection methods typically use a large amount of annotated training data,... [more] PRMU2021-31
pp.43-44
PRMU 2021-12-16
16:45
Online Online Verification of Cyclical Annealing for Object-Oriented Representation Learning
Atsushi Kobayashi (Waseda Univ.), Hideki Tsunashima (Waseda Univ./AIST), Takehiko Ohkawa (The Univ. of Tokyo), Hiroaki Aizawa (Hiroshima Univ.), Qiu Yue, Hirokatsu Kataoka (AIST), Shigeo Morishima (Waseda Univ.) PRMU2021-39
Object-oriented Representation Learning is a method for obtaining images for each object and background part from an ima... [more] PRMU2021-39
pp.83-87
QIT
(2nd)
2021-11-30
13:30
Online Online [Poster Presentation] Machine Learning techniques for unitary design classification:A comparative study
Yaswitha Gujju, Bo Yang, Dr. Yuko Kuroki, Dr. Hiroshi Imai (UTokyo)
The recent use of correlator functions to identify the degree of pseudorandomness in a qubit system opens up immense pos... [more]
PRMU 2021-08-26
10:00
Online Online Unsupervised non-rigid alignment for multiple noisy images
Takanori Asanomi, Kazuya Nishimura, Heon Song, Junya Hayashida (Kyushu Univ.), Hiroyuki Sekiguchi (Kyoto Univ.), Takayuki Yagi (Luxonus), Imari Sato (NII), Ryoma Bise (Kyushu Univ.) PRMU2021-7
We propose a deep non-rigid alignment network that can simultaneously perform non-rigid alignment and noise decompositio... [more] PRMU2021-7
pp.1-6
SIP 2021-08-23
13:25
Online Online A study on transfer learning in unsupervised anomalous sound detection based on deep metric learning considering variance of normal data
Hiroki Narita, Akira Tamamori (AIT) SIP2021-28
In recent years, anomaly detection research in the field of computer vision has focused on methods based on transfer lea... [more] SIP2021-28
pp.5-10
CS 2021-07-16
09:40
Online Online Joint Transmit Power and Beamforming Control based on Unsupervised Machine Learning for MIMO Wireless Communication Networks
Naoto Tamada, Yuyuan Chang, Kazuhiko Fukawa (Tokyo Tech) CS2021-29
In mobile communications, densely deployed cell systems are expected to improve the system capacity drastically. However... [more] CS2021-29
pp.63-68
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