| IEICE Technical Committee Submission System Conference Schedule |
Online Proceedings [Sign in] Tech. Rep. Archives |
All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] | 2022-02-21 16:45 |
Online | Online | A Note on Disentanglement Using Deep Generative Model Based on Variational Autoencoder
-- Introduction of Regularization Losses Based on Metrics of Disentangled Representation -- Nao Nakagawa, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
In this paper, we study disentangled representation learning using a deep generative model based on Variational Autoenco... [more] | |
| 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 |
| MI | 2021-03-16 09:30 |
Online | Online | Statistical modeling of pulmonary vasculatures in CT volumes using a deep generative model Yuki Saeki, Atshushi Saito (TUAT), Jean Cousty, Yukiko Kenmochi (LIGM/ UGE/ CNRS/ ESIEE Paris), Akinobu Shimizu (TUAT) MI2020-65 |
The purpose of this study is to build a statistical intensity model of pulmonary vasculatures in CT volumes. In this stu... [more] | MI2020-65 pp.80-81 |
| IBISML | 2021-03-03 14:50 |
Online | Online | Safe reinforcement learning in high-dimensional continuous spaces Takumi Umemoto (NIT), Tohgoroh Matsui (Chubu Univ.), Atsuko Mutoh, Koich Moriyama, Inuzuka Nobuhiro (NIT) IBISML2020-50 |
We propose a method to extend the reinforcement learning method (CSEQ) based on success probability and profit in contin... [more] | IBISML2020-50 pp.55-62 |
| HCS | 2021-01-23 14:10 |
Online | Online | Development of a generative model for face motion during dialogue Shota Takashiro, Yutaka Nakamura, Yusuke Nishimura, Hiroshi Ishiguro (Osaka Univ.) HCS2020-54 |
Various devices, such as smart speakers, have been developed to support human daily life. Among them, interactive robots... [more] | HCS2020-54 pp.12-16 |
| SIS | 2020-12-01 10:50 |
Online | Online | A study of anomalous sound detection using sound activity detection Yasuhiro Kanishima, Takashi Sudo (Toshiba) SIS2020-29 |
In anomalous sound detection that determines the operation of the device or the quality of the product based on the soun... [more] | SIS2020-29 pp.12-17 |
| CS | 2020-11-05 16:00 |
Online | Online + Central Community Center, Nonoichi Community Center (Primary: Online, Secondary: On-site) |
[Invited Talk]
PAPR and OOBE Suppression of OFDM Signal Using AutoEncoder Masaya Ohta, Reiya Kuwahara (OPU) CS2020-52 |
OFDM (Orthogonal Frequency Division Multiplexing) signals have characteristics of both high OOBE (Out-of-Band Emission) ... [more] | CS2020-52 pp.30-34 |
| IBISML | 2020-10-21 15:35 |
Online | Online | IBISML2020-24 |
The goal of this study is to understand the information processing mechanism in a deep neural network (DNN) as a curve $... [more] | IBISML2020-24 p.42 |
| MI | 2020-09-03 13:10 |
Online | Online | [Invited Talk]
Manifold modeling in embedded space for image restoration Tatsuya Yokota (Nitech) MI2020-27 |
In this invited talk, I will discuss convolutional neural networks, which have achieved remarkable results in various im... [more] | MI2020-27 pp.43-44 |
| IT | 2020-07-16 14:20 |
Online | Online | A Study on Trainable ISTA using Auto Encoder as Shrinkage Function for Image Recovery Kento Yokoyama, Satoshi Takabe, Tadashi Wadayama (NIT) IT2020-13 |
ISTA (Iterative Shrinkage-Thresholding Algorithm) is one of the basic algorithms used in compressed sensing to estimate ... [more] | IT2020-13 pp.13-18 |
| DE | 2020-06-27 12:15 |
Online | Online | Estimation of Sports Broadcast Situations based on Character-level Auto Encoder for Live Tweets Nodoka Fujimoto, Taketoshi Ushiama (Kyushu Univ.) DE2020-10 |
In this paper, we propose a method to generate vectors of each situation on video content from the latest small number o... [more] | DE2020-10 pp.53-58 |
| CS, CAS | 2020-02-28 09:25 |
Kumamoto | PAPR and OOBE Suppression of OFDM Signal Using Deep Learning Reiya Kuwahara, Masaya Ohta (OPU) CAS2019-114 CS2019-114 |
OFDM (Orthogonal Frequency Division Multiplexing) signals have characteristics of both high OOBE (Out-of-Band Emission) ... [more] | CAS2019-114 CS2019-114 pp.95-98 |
|
| EST | 2020-01-30 09:40 |
Oita | Beppu International Convention Center | Embedded object identification from ground penetrating radar image by semi-supervised learning using variational auto-encoder Tomoyuki Kimoto (NIT, Oita), Jun Sonoda (NIT, Sendai) EST2019-80 |
Recently, deterioration of social infrastructures such as tunnels and bridges becomes serious social problem. It is requ... [more] | EST2019-80 pp.7-12 |
| MI | 2020-01-29 10:05 |
Okinawa | OKINAWAKEN SEINENKAIKAN | A study of generalized generation of image features for computer-aided detection systems based on unsupervised learning with normal datasets
-- Experimental evaluations of feature generation by small datasets -- Kazuyuki Ushifusa, Mitsutaka Nemoto(, Yuichi Kimura, Takashi Nagaoka, Takahiro Yamada, Atsuko Tanaka (Kindai Uni.), Naoto Hayashi (The Uni of Tokyo Hosp) MI2019-68 |
In a computer-aided detection system, image features are essential factors. In this study, we propose an image feature g... [more] | MI2019-68 pp.15-18 |
| EA | 2019-12-12 14:25 |
Fukuoka | Kyushu Inst. Tech. | Performance improvement of speech enhancement network by multitask learning including noise information Haruki Tanaka (NITTC), Yosuke Sugiura, Nozomiko Yasui, Tetsuya Shimamura (Saitama Univ.), Ryoichi Miyazaki (NITTC) EA2019-70 |
In the signal processing field, there is a growing interest in speech enhancement.Recently, a lot of speech enhancement ... [more] | EA2019-70 pp.31-36 |
| NC, MBE | 2019-12-06 15:40 |
Aichi | Toyohashi Tech | Prevention of redundant representations and of the black box in stacked autoencoders Masumi Ishikawa (Kyutech) MBE2019-56 NC2019-47 |
Recent progress in deep learning (DL) is remarkable and its recognition capability is said to surpass that of humans. Th... [more] | MBE2019-56 NC2019-47 pp.67-72 |
| AI | 2019-07-22 15:55 |
Hokkaido | A Recommendation System using Auto-encoders for Data Divided on Item Densities Go Tanioka, Akihiro Inokuchi (KGU) AI2019-14 |
In recent years, deep learning have attracted attention in the field of machine learning, and its applications to the re... [more] | AI2019-14 pp.71-75 |
|
| SeMI, RCS, NS, SR, RCC (Joint) |
2019-07-11 15:00 |
Osaka | I-Site Nanba（Osaka） | Deep learning-based classification for the automatic of eNodeB state management in LTE networks Kazuki Hara (Tsukuba Univ.), Kohei Shiomoto (TCU), Chin Lam Eng, Sebastian Backstad (Ericsson Japan) RCC2019-41 NS2019-77 RCS2019-134 SR2019-53 SeMI2019-50 |
It is crucial to identify the cause immediately when a failure occurs at base station of mobile communication. However, ... [more] | RCC2019-41 NS2019-77 RCS2019-134 SR2019-53 SeMI2019-50 pp.145-150(RCC), pp.171-176(NS), pp.167-172(RCS), pp.177-182(SR), pp.159-164(SeMI) |
| SIS, IPSJ-AVM, ITE-3DMT [detail] | 2019-06-13 13:15 |
Nagasaki | Fukue Culture Center | Autoencoders having surplus neurons in a hidden layer Akihiro Suzuki, Hakaru Tamukoh (KYUTECH) SIS2019-3 |
Unknown data is not compatible with a supervised training. This study employ autoencoders (AEs) to detect unknwon data. ... [more] | SIS2019-3 pp.11-16 |
| SIS | 2019-03-06 14:30 |
Tokyo | Tokyo Univ. Science, Katsushika Campus | Complementary Color Reconstruction by Autoencoders Akihiro Suzuki, Hakaru Tamukoh (Kyutech) SIS2018-41 |
This study proposes a novel training method for autoencoders (AEs) that gives the AEs complementary color images as targ... [more] | SIS2018-41 pp.23-28 |
[Return to Top Page]