| 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 # | |
| MI | 2023-07-03 13:00 |
Miyagi | Tohoku Univ. Sakura Hall | [Special Talk]
Transition of Medical Imaging Koichi Ito (Tohoku Univ.)  MI2023-10 |
Over the past decade, research in medical image processing has dramatically changed. In particular, feature extraction u... [more] | MI2023-10 p.11 |
| SR | 2023-05-11 15:10 |
Hokkaido | Center of lifelong learning Kiran (Higashi Muroran) (Primary: On-site, Secondary: Online) |
[Short Paper]
Continuous Compressible Deep Joint Source Channel Coding Junichiro Yamada, Katsuya Suto (UEC) SR2023-11 |
Deep Joint Source Channel Coding (DeepJSCC), which designs a source and channel coding model with deep neural networks a... [more] | SR2023-11 pp.58-60 |
| MI | 2023-03-06 13:15 |
Okinawa | OKINAWA SEINENKAIKAN (Primary: On-site, Secondary: Online) |
[Short Paper]
Improvement of Small Organ Accuracy in Multi-Organ Segmentation of Abdominal CT Images Using 2.5D Deformable Convolutional CNN Yuya Okumura, Hiroyuki Kudo, Hotaka Takizawa (Univ of Tsukuba) MI2022-80 |
In multi-organ segmentation of abdominal CT images using deep learning, small organs such as the pancreas are difficult ... [more] | MI2022-80 pp.38-39 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2023-03-02 09:00 |
Hokkaido | Future University Hakodate (Primary: On-site, Secondary: Online) |
Toward Regularizing Neural Networks with Meta-Learning Generative Models Shin'ya Yamaguchi (NTT/Kyoto Univ.), Daiki Chijiwa, Sekitoshi Kanai, Atsutoshi Kumagai (NTT), Hisashi Kashima (Kyoto Univ.) PRMU2022-58 IBISML2022-65 |
This paper investigates methods for improving generative data augmentation for deep learning. Generative data augmentati... [more] | PRMU2022-58 IBISML2022-65 pp.1-6 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2023-03-02 11:40 |
Hokkaido | Future University Hakodate (Primary: On-site, Secondary: Online) |
Novel Adversarial Attacks Based on Embedding Geometry of Data Manifolds Masahiro Morita, Hajime Tasaki, Jinhui Chao (Chuo Univ.) PRMU2022-84 IBISML2022-91 |
It has been shown recently that adversarial examples inducing misclassification by deep neural networks exist in the ort... [more] | PRMU2022-84 IBISML2022-91 pp.140-145 |
| EMM | 2023-03-02 16:00 |
Nagasaki | Fukue culture hall (Primary: On-site, Secondary: Online) |
[Invited Talk]
Image transformation with random numbers for reliable AI Hitoshi Kiya (TMU) EMM2022-87 |
The combined use of deep neural networks (DNNs) and images transformed with random sequences has given a new insight for... [more] | EMM2022-87 pp.107-109 |
| SIS | 2023-03-02 13:30 |
Chiba | Chiba Institute of Technology (Primary: On-site, Secondary: Online) |
An image watermarking method using adversarial perturbations Sei Takano, Mitsuji Muneyasu, Soh Yoshida (Kansai Univ.) SIS2022-43 |
The performance of convolutional neural networks (CNNs) has been dramatically improved in recent years, and they have at... [more] | SIS2022-43 pp.15-20 |
| DC | 2023-02-28 14:25 |
Tokyo | Kikai-Shinko-Kaikan Bldg (Primary: On-site, Secondary: Online) |
Test Point Selection Method Using Graph Neural Networks and Deep Reinforcement Learning Shaoqi Wei, Kohei Shiotani, Senling Wang, Hiroshi Kai, Yoshinobu Higami, Hiroshi Takahashi (Ehime Univ.) DC2022-87 |
It is well known that selecting the optimal test point to maximize the fault coverage is NP-hard. Conventional heuristic... [more] | DC2022-87 pp.27-32 |
| IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2023-02-21 15:30 |
Hokkaido | Hokkaido Univ. | A Study on Adaptation Methods for Universal Deep Image Compression Koki Tsubota, Kiyoharu Aizawa (UTokyo) ITS2022-56 IE2022-73 |
In this study, we tackle universal deep image compression, which aims to compress images in arbitrary domains such as li... [more] | ITS2022-56 IE2022-73 pp.77-82 |
| IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2023-02-22 10:15 |
Hokkaido | Hokkaido Univ. | Generation Method of Targeted Adversarial Examples using Gradient Information for the Target Class of the Image Ryo Kumagai, Shu Takemoto, Yusuke Nozaki, Masaya Yoshikawa (Meijo Univ.) ITS2022-61 IE2022-78 |
With the advancement of AI technology, the vulnerability of AI system is pointed out. The adversarial examples (AE), whi... [more] | ITS2022-61 IE2022-78 pp.107-111 |
| EMM | 2023-01-26 09:55 |
Miyagi | Tohoku Univ. (Primary: On-site, Secondary: Online) |
On the Transferability of Adversarial Examples between Isotropic Network and CNN models Miki Tanaka (Tokyo Metropolitan Univ.), Isao Echizen (NII), Hitoshi Kiya (Tokyo Metropolitan Univ.) EMM2022-62 |
Deep neural networks are well known to be vulnerable to adversarial examples (AEs). In addition, AEs generated for a sou... [more] | EMM2022-62 pp.7-12 |
| PRMU | 2022-12-15 10:45 |
Toyama | Toyama International Conference Center (Primary: On-site, Secondary: Online) |
DN4C
-- An Interactive Image Segmentation System Combining Deep Neural Network and Nearest Neighbor Classifier -- Toshikazu Wada, Koji Kamma (Wakayama University) PRMU2022-35 |
Color/texture based image segmentation can be widely applied to the images for product and/or medical inspection, remote... [more] | PRMU2022-35 pp.19-24 |
| HCGSYMPO (2nd) |
2022-12-14 - 2022-12-16 |
Kagawa | Onsite (Sunport Takamatsu) and Online (Primary: On-site, Secondary: Online) |
Computational Modeling with Geometric Morphometrics and Deep Neural Networks
-- An approach Methodology for Identifying Facial Impression Factors -- Takanori Sano, Hideaki Kawabata (Keio Univ.) |
Numerous studies have been conducted in psychology on the factors that influence facial impressions. In recent years, st... [more] | |
| SIS | 2022-12-05 14:50 |
Osaka | (Primary: On-site, Secondary: Online) |
A Method of Automatic Wall Detection from Room Images by Deep Neural Networks Shunya Shimegi, Kaoru Arakawa (Meiji Univ.) SIS2022-27 |
In order to design interior renovation easily，a method of automatic wall area detection is proposed using deep neural ne... [more] | SIS2022-27 pp.21-25 |
| DC, SS | 2022-10-25 14:40 |
Fukushima | (Primary: On-site, Secondary: Online) |
Comparison of the Coverage Indicators of Evaluation Data for the Convolutional Neural Networks Yuto Yokoyama, Kozo Okano, Shinpei Ogata (Shinshu Univ.), Shin Nakazima (NII) SS2022-27 DC2022-33 |
Neuron Coverage (NC) was proposed as a measure to quantify the usefulness of evaluation data against Deep Neural Network... [more] | SS2022-27 DC2022-33 pp.29-34 |
| IBISML | 2022-09-15 14:00 |
Kanagawa | Keio Univ. (Yagami Campus) (Primary: On-site, Secondary: Online) |
Interpretable Model Combining statements and DNN Ryo Okuda, Yuya Yoshikawa (STAIR) IBISML2022-36 |
In this study, we propose a method that achieves both interpretability of Decision Tree and the prediction accuracy of D... [more] | IBISML2022-36 pp.25-30 |
| MI | 2022-09-15 10:00 |
Kanagawa | (Primary: On-site, Secondary: Online) |
Automatic Multi-Measure Classification of Hip Osteoarthritis Based on Digitally-Reconstructed Radiographs using Deep Learning Masachika Masuda, Mazen Soufi, Yoshito Otake (NAIST), Keisuke Uemura (Osaka Univ.), Masaki Takao (Ehime Univ.), Nobuhiko Sugano (Osaka Univ.), Yoshinobu Sato (NAIST) MI2022-49 |
Hip Osteoarthritis (HOA) is usually diagnosed by radiographs. In addition to the degree of cartilage degeneration, the d... [more] | MI2022-49 pp.1-4 |
| SIP | 2022-08-26 14:26 |
Okinawa | Nobumoto Ohama Memorial Hall (Ishigaki Island) (Primary: On-site, Secondary: Online) |
Generation method of Adversarial Examples using XAI Ryo Kumagai, Shu Takemoto, Yusuke Nozaki, Masaya Yoshikawa (Meijo Univ.) SIP2022-72 |
With the advancement of AI technology, AI can be applied to various fields. Therefore the accountability for the decisio... [more] | SIP2022-72 pp.115-120 |
| EA, ASJ-H | 2022-08-04 15:15 |
Miyagi | (Primary: On-site, Secondary: Online) |
[Invited Talk]
Audio Source Separation Combining Wavelet Transform and Deep Neural Network Tomohiko Nakamura (Univ. Tokyo) EA2022-32 |
Audio source separation is a technique of separating an observed audio signal into individual source signals. The use of... [more] | EA2022-32 p.25 |
| SS, IPSJ-SE, KBSE [detail] | 2022-07-29 13:25 |
Hokkaido | Hokkaido-Jichiro-Kaikan (Sapporo) (Primary: On-site, Secondary: Online) |
Fault Localization for RNNs Based on Probabilistic Automata and n-grams Yuta Ishimoto, Masanari Kondo, Naoyasu Ubayashi, Yasutaka Kamei (Kyushu Univ.) SS2022-10 KBSE2022-20 |
If deep learning models misbehave, serious accidents may occur.Previous studies have proposed approaches to overcome suc... [more] | SS2022-10 KBSE2022-20 pp.55-60 |
[Return to Top Page]