Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
SIS |
2024-03-14 14:50 |
Kanagawa |
Kanagawa Institute of Technology (Primary: On-site, Secondary: Online) |
Improvement of Detection Accuracy for Detection of Calcification Regions in Dental Panoramic Radiographs Using LVAT Naoki Ikeda, Sei Takano, Mitsuji Muneyasu, Soh Yoshida, Akira Asano (Kansai Univ.), Nanae Dewake, Nobuo Yoshinari (Matsumoto Dental Univ.), Keiichi Uchida (Matsumoto Dental Univ. Hospital) SIS2023-50 |
Carotid arteries on dental panoramic radiographs may show areas of calcification, a sign of vascular disease. The sudden... [more] |
SIS2023-50 pp.27-32 |
EMM |
2024-01-17 10:55 |
Miyagi |
Tohoku Univ. (Primary: On-site, Secondary: Online) |
Detecting Adversarial Examples using Filtering Operation Based on JPEG-Compression-Derived Distortion Kenta Tsunomori (Okayama Univ.), Minoru Kuribayashi (Tohoku Univ.), Nobuo Funabiki (Okayama Univ.) EMM2023-87 |
Image classifiers based on convolutional neural networks are caused misclassification by adversarial perturbations. In t... [more] |
EMM2023-87 pp.38-43 |
MI, MICT |
2023-11-14 15:40 |
Fukuoka |
|
Improving image quality of sparse-view micro-CT using Wasserstein GAN Naoki Ikezawa, Takayuki Okamoto, Hideaki Haneishi (Chiba Univ.) MICT2023-36 MI2023-29 |
Applications of micro-CT in pathology and histology have been studied in recent years, and we need to shorten the scanni... [more] |
MICT2023-36 MI2023-29 pp.45-47 |
NS, IN, CS, NV (Joint) |
2023-09-08 09:00 |
Miyagi |
Tohoku University (Primary: On-site, Secondary: Online) |
Demonstrating Data Poisoning Attacks on Machine Learning Models with Multi-Sensor Inputs Shyam Maisuria, Yuichi Ohsita, Masayuki Murata (Osaka Univ.) IN2023-31 |
Data poisoning attacks pose a significant threat to the integrity and reliability of machine learning models. These atta... [more] |
IN2023-31 pp.8-13 |
MI |
2023-03-07 15:12 |
Okinawa |
OKINAWA SEINENKAIKAN (Primary: On-site, Secondary: Online) |
Conversion to pseudo-CT images from phalanges CR images using adversarial generation networks Tomoaki Ushikoshi, Takaharu Yamazaki (SIT), Kazuaki Tanaka (Neomedical), Keizo Fukumoto (Saitama Jikei Hospital) MI2022-119 |
In this study, we perform conversion to pseudo-CT images from phalanges CR images using adversarial generative network. ... [more] |
MI2022-119 pp.184-189 |
IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] |
2023-02-22 09:45 |
Hokkaido |
Hokkaido Univ. |
Probabilistic Approach towards Theoretical Understanding for Adversarial Training Soichiro Kumano (UTokyo), Hiroshi Kera (Chiba Univ.), Toshihiko Yamasaki (UTokyo) ITS2022-59 IE2022-76 |
In this paper, we provide the first theoretical analysis of the training dynamics of adversarial training of deep neural... [more] |
ITS2022-59 IE2022-76 pp.95-100 |
SeMI, SeMI (Joint) |
2023-01-20 10:20 |
Tokushima |
Naruto grand hotel (Primary: On-site, Secondary: Online) |
Arterial Blood Pressure Waveform Estimation from Photoplethysmogram under Inter-subject Paradigm by U-Net and Domain Adversarial Training Rikuto Yoshizawa, Kohei Yamamoto, Tomoaki Ohtsuki (Keio) SeMI2022-96 |
Blood pressure (BP) estimation methods using photoplethysmogram (PPG) signals based on deep learning models have been ac... [more] |
SeMI2022-96 pp.113-118 |
SIS |
2022-12-05 15:10 |
Osaka |
(Primary: On-site, Secondary: Online) |
Application of Adversarial Training in Detection of Calcification Regions from Dental Panoramic Radiographs Sei Takano, Mitsuji Muneyasu, Soh Yoshida, Akira Asano (Kansai Univ.), Keiichi Uchida (Matsumoto Dental Univ. Hospital) SIS2022-28 |
Calcification regions that are a sign of vascular diseases may be observed on dental panoramic radiographs. The finding ... [more] |
SIS2022-28 pp.26-31 |
VLD, DC, RECONF, ICD, IPSJ-SLDM [detail] |
2022-11-30 16:40 |
Kumamoto |
(Primary: On-site, Secondary: Online) |
Evaluation of Model Quantization Method on Vitis-AI for Mitigating Adversarial Examples Yuta Fukuda, Kota Yoshida, Takeshi Fujino (Ritsumeikan Univ.) VLD2022-51 ICD2022-68 DC2022-67 RECONF2022-74 |
Adversarial examples (AEs) are security threats in deep neural networks (DNNs). One of the countermeasures is adversaria... [more] |
VLD2022-51 ICD2022-68 DC2022-67 RECONF2022-74 pp.182-187 |
PRMU |
2022-09-15 10:45 |
Kanagawa |
(Primary: On-site, Secondary: Online) |
Effect validation of adversarial auxiliary classifier for video disentanglement Takeshi Haga, Hiroshi Kera, Kazuhiko Kawamoto (Chiba Univ) PRMU2022-21 |
The Disentanglement of sequential data such as video requires inductive biases to separate static latent variables from ... [more] |
PRMU2022-21 pp.67-71 |
MI |
2022-07-08 16:00 |
Hokkaido |
(Primary: On-site, Secondary: Online) |
[Short Paper]
Unsupervised Domain Adaptation for Liver Tumor Detection in Multi-Phase CT images Using Adversarial Learning with Maximum Square Loss Rahul Kumar Jain (Ritsumeikan Univ.), Takahiro Sato, Taro Watasue, Tomohiro Nakagawa (tiwaki), Yutaro Iwamoto (Ritsumeikan Univ.), Xianhua Han (Yamaguchi Univ.), Lanfen Lin, Hongjie Hu (Zhejiang Univ.), Yen-Wei Chen (Ritsumeikan Univ.) MI2022-37 |
Liver tumor detection in multi-phase CT images is essential in computer-aided diagnosis. Deep learning has been widely ... [more] |
MI2022-37 pp.22-23 |
NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] |
2022-06-27 15:30 |
Okinawa |
(Primary: On-site, Secondary: Online) |
Evaluating and Enhancing Reliabilities of AI-Powered Tools
-- Adversarial Robustness -- Jingfeng Zhang (RIKEN-AIP) NC2022-4 IBISML2022-4 |
When we deploy models trained by standard training (ST), they work well on natural test data. However, those models cann... [more] |
NC2022-4 IBISML2022-4 pp.20-46 |
SIS, IPSJ-AVM |
2022-06-09 15:00 |
Fukuoka |
KIT(Wakamatsu Campus) (Primary: On-site, Secondary: Online) |
[Invited Talk]
Advanced applications of machine learning techniques towards high-performance and cost-effective visual inspection AI Terumasa Tokunaga (Kyutech) SIS2022-6 |
Visual inspection is an essential step for quality control in manufacturing. Recently, many researchers have shown great... [more] |
SIS2022-6 p.30 |
PRMU, IPSJ-CVIM |
2022-03-10 17:30 |
Online |
Online |
Adversarial Training: A Survey Hiroki Adachi, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi (Chubu Univ.) PRMU2021-73 |
Adversarial training (AT) is a training method that aims to obtain a robust model for defencing the adversarial attack b... [more] |
PRMU2021-73 pp.78-90 |
IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] |
2022-02-21 12:45 |
Online |
Online |
Regularizing Generative Adversarial Networks with Internal Representation of Generators Yusuke Hara, Toshihiko Yamasaki (UTokyo) ITS2021-29 IE2021-38 |
In training generative adversarial networks, maintaining the criteria of the discriminator stably is crucial to training... [more] |
ITS2021-29 IE2021-38 pp.25-30 |
NLP, MICT, MBE, NC (Joint) [detail] |
2022-01-23 11:45 |
Online |
Online |
Adversarial Training with Knowledge Distillation considering Intermediate Feature Representation in CNNs Hikaru Higuchi (The Univ. of Electro-Communications), Satoshi Suzuki (former NTT), Hayaru Shouno (The Univ. of Electro-Communications) NC2021-44 |
Adversarial examples are one of the vulnerability attacks to the convolution neural network (CNN). The adversarialexampl... [more] |
NC2021-44 pp.59-64 |
IBISML |
2022-01-17 10:40 |
Online |
Online |
Automatic Makeup Transfer with GANs and Its Quantitative Evaluation Cuilin Wang, Jun'ichi Takeuchi (Kyushu Univ.) IBISML2021-20 |
Transferring makeup from a reference image with makeup to a source image without makeup has a wide range of application ... [more] |
IBISML2021-20 pp.17-22 |
IBISML |
2022-01-18 14:00 |
Online |
Online |
Robustness to Adversarial Examples by Mixtures of L1 Regularazation Models Hironobu Takenouchi, Junichi Takeuchi (Kyushu Univ.) IBISML2021-26 |
We propose a method of adversarial training using L1 regularizationfor image classification.It is known that L1 regulari... [more] |
IBISML2021-26 pp.61-66 |
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 |
IPSJ-AVM, CS, IE, ITE-BCT [detail] |
2021-11-25 10:25 |
Online |
Online |
wganBCS: Block-wise image compressive sensing and reconstruction model using adversarial training to eliminate block effects Boyan Chen (Hosei Univ./NPU), Kaoru Uchida (Hosei Univ.) CS2021-60 IE2021-19 |
The famous block-wise compressive sensing (BCS) paradigm can greatly reduce the memory consumption of sensing
matrix co... [more] |
CS2021-60 IE2021-19 pp.1-6 |