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All Technical Committee Conferences  (Searched in: Recent 10 Years)

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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 22  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
NC, MBE
(Joint)
2025-03-07
12:45
Tokyo (Tokyo, Online)
(Primary: On-site, Secondary: Online)
GhostNet with CSAR Block
Haruto Kobayashi, Yuta Kobayashi, Yukari Yamauchi (Nihon Univ) NC2024-84
In this study, we aimed to enhance the performance of GhostNet, a model proposed by Han et al., while maintaining its li... [more] NC2024-84
pp.117-120
EMD 2025-02-28
14:15
Tokyo NIT (Tokyo) Analysis with deep learning scheme on rotation phenomena of speckle patterns in an output light spot from an optical fiber
Ryusei Sato, Makoto Hasegawa (Chitose Inst. of Science and Technology) EMD2024-39
When laser light beams are allowed to propagate from one end of a multi-mode optical fiber (MMF) to the other end, irreg... [more] EMD2024-39
pp.42-47
NLP, CCS 2024-06-06
15:20
Fukuoka West Japan General Exhibition Center AIM (Fukuoka) An Efficient Deep Learning Method by Sequential Insertion of Hidden Layers
Kentaro Toki, Hidehiro Nakano (Tokyo City Univ.) NLP2024-24 CCS2024-11
In recent deep learning, deep neural networks are trained using residual connections. However, training deep models requ... [more] NLP2024-24 CCS2024-11
pp.44-48
SR 2024-05-21
12:50
Kagoshima Yokacenter (Kagoshima) (Kagoshima, Online, Online, Online)
(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) SR2024-17
Nowadays the Fifth-Generation Mobile communications system (5G) and the other mobile communications systems have spread ... [more] SR2024-17
p.70
EMD 2024-03-01
13:45
Chiba   (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 (Nagasaki, Online)
(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 (Kanagawa, Online)
(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 (Kanagawa, Online)
(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 (Ishikawa, 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 (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 (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 (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 (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 (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 (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 (Okinawa) 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 (Okinawa) 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 (Okinawa) 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 (Tokyo) 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 (Tokyo) 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
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