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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| SR | 2024-05-21 12:50 |
Kagoshima | Yokacenter (Kagoshima) (Primary: On-site, Secondary: Online) |
[Invited Talk]
A Study on Utilization of Machine Learning in Effective Use of Frequency Resources Teruji Ide (N I T, Kagoshima College) |
(To be available after the conference date) [more] | |
| EMD | 2024-03-01 13:45 |
Chiba | A study on analysis of rotation phenomena of MMF speckle patterns with deep learning Ryusei Sato, Makoto Hasegawa (Chitose Inst. of Science and Technology)  EMD2023-41 |
When laser light beams are allowed to propagate from one end of an optical fiber to the other end and further to be outp... [more] | EMD2023-41 pp.13-18 |
|
| DC | 2023-12-08 13:30 |
Nagasaki | ARKAS SASEBO (Primary: On-site, Secondary: Online) |
DC2023-87 |
(To be available after the conference date) [more] | DC2023-87 pp.1-6 |
| MI | 2022-09-15 14:15 |
Kanagawa | (Primary: On-site, Secondary: Online) |
Study of Detecting Oral Disease Using Oral Images Acquired by a Dermoscope and Deep Learning Yuta Suzuki, Jun Ohya (Waseda Univ.), Toshihiro Okamoto, Nobuyuki Kaibuchi, Katsuhisa Sakaguchi, Kitaro Yoshimitsu (TWMU), Eiji Fukuzawa (Waseda U./Yazaki) MI2022-58 |
In this study, we focused on a device called a dermoscope as a simple and minimally invasive diagnostic tool instead of ... [more] | MI2022-58 pp.39-44 |
| PRMU | 2022-09-14 16:00 |
Kanagawa | (Primary: On-site, Secondary: Online) |
Convolutional Skip Connection for Compressing DNNs with Branched Architectures Koji Kamma, Toshikazu Wada (Wakayama Univ.) PRMU2022-16 |
Although Deep Neural Network (DNN) is a core technology in Computer Vision, it is difficult to implement DNN models beca... [more] | PRMU2022-16 pp.37-42 |
| NS, SR, RCS, SeMI, RCC (Joint) |
2022-07-15 14:30 |
Ishikawa | The Kanazawa Theatre + Online (Primary: On-site, Secondary: Online) |
Communication Size Reduction of Federated Learning based on Neural ODE Model Yuto Hoshino, Hiroki Kawakami, Hiroki Matsutani (Keio Univ.) NS2022-59 |
(To be available after the conference date) [more] | NS2022-59 pp.157-162 |
| IMQ | 2022-05-27 14:25 |
Tokyo | Classification-ESRGAN
-- Synthesis of super-resolution images based on subject categorization -- Jingan Liu, Atsumu Harada, Naiwala P. Chandrasiri (Kogakuin Univ.) IMQ2022-3 |
In recent years, super-resolution techniques have been significantly developed based on deep learning. In particular, GA... [more] | IMQ2022-3 pp.12-17 |
|
| IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] | 2022-02-21 16:15 |
Online | Online | A Note on Realizing Adversarial Defense Based on Regularization of Multi-stage Squeeze-and-Excitation Features Jiahuan Zhang, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
Regularizing deep features is a common adversarial defense method. However, the existing methods do not further explore ... [more] | |
| RCS, SIP, IT | 2022-01-21 10:55 |
Online | Online | A lossless audio codec based on hierarchical residual prediction Taiyo Mineo, Shouno Hayaru (UEC) IT2021-71 SIP2021-79 RCS2021-239 |
In this study, we propose a novel lossless audio codec that has precise predictive performance from the neural network a... [more] | IT2021-71 SIP2021-79 RCS2021-239 pp.239-244 |
| CS | 2021-10-14 10:15 |
Online | Online | An Experimental Study on Improving Accuracy of Location Estimation in Finger Print Using CNN and ResNet Yu Sakanishi, Satoru Aikawa, Shinichiro Yamamoto, Yuta Sakai (Univ of Hyogo) CS2021-52 |
Recently, indoor navigation system is one of the important technologies. We are studying an indoor location estimation ... [more] |
CS2021-52 pp.1-5 |
| MI | 2021-07-09 10:30 |
Online | Online | Applying Convolutional Network to Predict Pathology of Postchemotherapy Retroperitoneal Nodal Masses in Germ Cell tumors Yoshimasa Iwano, Satoshi Nitta (Univ. of Tsukuba), Takahiro Kojima (Aichi Cancer Center), Hideki Kakeya (Univ. of Tsukuba) MI2021-15 |
The main treatment for advanced testicular cancer is chemotherapy and following surgical resection of residual masses. T... [more] | MI2021-15 pp.25-30 |
| MVE, IPSJ-CVIM | 2021-01-22 15:35 |
Online | Online | [Short Paper]
Basic examination about wiring status judgment of switchboard by image recognition Keishi Nishimoto, Takeshi Hirama (itic.pref.ibaraki.jp) MVE2020-40 |
In the manufacturing industry, especially in the field of wiring work, the shortage of workers is an issue, and even beg... [more] | MVE2020-40 pp.45-46 |
| NC, IBISML, IPSJ-MPS, IPSJ-BIO [detail] | 2019-06-18 13:55 |
Okinawa | Okinawa Institute of Science and Technology | Additive or Concatenating Skip-connections Overcome the Degradation Problem of the Classic Feedforward Neural Network Yasutaka Furusho, Kazushi Ikeda (NAIST) NC2019-17 IBISML2019-15 |
The classic feedforward neural networks like the multilayer perceptron (MLP) degrades its empirical risk by training eve... [more] | NC2019-17 IBISML2019-15 pp.75-80(NC), pp.97-102(IBISML) |
| NC, IBISML, IPSJ-MPS, IPSJ-BIO [detail] | 2019-06-18 14:20 |
Okinawa | Okinawa Institute of Science and Technology | ResNet and Batch-normalization Improve Data Separation Ability Yasutaka Furusho, Kazushi Ikeda (NAIST) NC2019-18 IBISML2019-16 |
The skip-connection and the batch-normalization (BN) in ResNet enable an extreme deep neural network to be trained with ... [more] | NC2019-18 IBISML2019-16 pp.81-86(NC), pp.103-108(IBISML) |
| NC, IBISML, IPSJ-MPS, IPSJ-BIO [detail] | 2019-06-18 14:45 |
Okinawa | Okinawa Institute of Science and Technology | Theoretical Analysis of the Fixup Initialization for Fast Convergence and High Generalization Ability Yasutaka Furusho, Kazushi Ikeda (NAIST) NC2019-19 IBISML2019-17 |
The Fixup initialization is a new initialization method of ResNet for a fast convergence with a high learning rate of SG... [more] | NC2019-19 IBISML2019-17 pp.87-92(NC), pp.109-114(IBISML) |
| IBISML | 2019-03-05 14:00 |
Tokyo | RIKEN AIP | Expressive power of skip connection and network architecture Jumpei Nagase, Tetsuya Ishiwata (Shibaura Inst. of Tech.) IBISML2018-106 |
Model design is one of research topics in deep learning. Proposing a better model has been extensively studied, but ther... [more] | IBISML2018-106 pp.9-15 |
| IBISML | 2019-03-06 10:00 |
Tokyo | RIKEN AIP | Effects of Batch-normalization on Fisher Information Matrix of ResNet Yasutaka Furusho, Kazushi Ikeda (NAIST) IBISML2018-110 |
ResNet have intensively been studied and many techniques have been used for better performance. Batch-normalization (BN... [more] |
IBISML2018-110 pp.39-44 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2018-09-21 10:00 |
Fukuoka | [Short Paper]
Computer-Aided Diagnosis of Liver Cancers Using Deep Learning with Fine-tuning Weibin Wang (Ritsumeikan Univ.), Dong Liang, Lanfen Lin, Hongjie Hu, Qiaowei Zhang, Qingqing Chen (Zhejiang Univ.), Yutaro lwamoto, Xianhua Han, Yen-Wei Chen (Ritsumeikan Univ.) PRMU2018-57 IBISML2018-34 |
Liver cancer is one of the leading causes of death world-wide. Computer-aided diagnosis plays an important role in liver... [more] | PRMU2018-57 IBISML2018-34 pp.139-140 |
|
| PRMU, CNR | 2018-02-19 14:20 |
Wakayama | Verification of Shake Drop for neural network with deep residual block Hiroki Morishita, Katsufumi Inoue, Michifumi Yoshioka (Osaka Prefecture Univ.) PRMU2017-157 CNR2017-35 |
(To be available after the conference date) [more] | PRMU2017-157 CNR2017-35 pp.71-76 |
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