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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 44  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
PRMU 2022-12-16
14:40
Toyama Toyama International Conference Center
(Primary: On-site, Secondary: Online)
Data Augmentation
Shumpei Takezaki (Kyushu Univ.), Kiyohito Tanaka (Kyoto Second Red Cross Hospital), Seiichi Uchida, Takeaki Kadota (Kyushu Univ.)
(To be available after the conference date) [more]
CCS 2022-11-17
14:55
Mie
(Primary: On-site, Secondary: Online)
Improvement of financial machine learning by fine-tuning using multiple time scales
Kazuki Amagai (Ibaraki Univ.), Riku Tanaka (Daiwa Asset Management), Tomoya Suzuki (Ibaraki Univ.) CCS2022-47
In asset management businesses such as operating mutual funds, medium or long-term investments are common in terms of op... [more] CCS2022-47
pp.19-24
CCS 2022-11-18
15:20
Mie
(Primary: On-site, Secondary: Online)
Multi-domain translation from few data by CycleGAN applying data augmentation
Syuhei Kanzaki, Hidehiro Nakano (Tokyo City Univ.) CCS2022-59
In machine learning and deep learning, a huge amount of data is required for training. The image generation model GAN ex... [more] CCS2022-59
pp.81-84
PRMU 2022-09-14
10:30
Kanagawa
(Primary: On-site, Secondary: Online)
Performance Evaluation of Data Augmentation Using Face Parsing for Improving Face Recognition
Hiroya Kawai, Koichi Ito (Tohoku Univ.), Hwann-Tzong Chen (NHTU), Takafumi Aoki (Tohoku Univ.) PRMU2022-12
Face recognition is one of the most promising methods to recognize individuals. Since the recognition accuracy is degrad... [more] PRMU2022-12
pp.13-18
PRMU 2022-09-14
15:45
Kanagawa
(Primary: On-site, Secondary: Online)
Human Pose Transfer with Reduced Color Transfer by Occlusion for Person Re-Identification
Masaki Kishibe, Toshikazu Wada (Wakayama Univ.) PRMU2022-15
Human pose transfer is the task that transforms a person image from the source pose to a given target pose, and is usefu... [more] PRMU2022-15
pp.31-36
PRMU 2022-09-14
16:15
Kanagawa
(Primary: On-site, Secondary: Online)
Data Augmentation with Style Transfer for Fossil Image Segmentation
Akihiro Waza (Osaka Metropolitan Univ.), Yuya Inamura (Osaka Prefecture Univ.), Katsufumi Inoue, Michifumi Yoshioka, Toshihiro Yamada (Osaka Metropolitan Univ.) PRMU2022-17
Fossils are extremely important materials in evolutionary biology and earth science. However, it is necessary to have sp... [more] PRMU2022-17
pp.43-48
IN, CCS
(Joint)
2022-08-04
10:00
Hokkaido Hokkaido University(Centennial Hall)
(Primary: On-site, Secondary: Online)
Investigation on Applying Data Augmentation to CycleGAN
Syuhei Kanzaki, Hidehiro Nakano (Tokyo City Univ.) CCS2022-26
In machine learning and deep learning, a huge amount of data is required for training. The image generation model GAN ex... [more] CCS2022-26
pp.1-5
IA, ICSS 2022-06-23
14:30
Nagasaki Univ. of Nagasaki
(Primary: On-site, Secondary: Online)
Discussion about improving a detection accuracy of malware variants using time series differences in latent representation.
Atsushi Shinoda, Hajime Shimada, Yukiko Yamaguti (Nagoya Univ.), Hirokazu Hasegawa (NII) IA2022-4 ICSS2022-4
Today, computers are used for various purposes to support people's daily lives. Therefore, the existence of malware that... [more] IA2022-4 ICSS2022-4
pp.19-24
PRMU, IPSJ-CVIM 2022-03-11
14:30
Online Online Background Mixup Data Augmentation for Hand and Object-in-Contact Detection
Koya Tango, Takehiko Ohkawa, Ryosuke Furuta, Yoichi Sato (UTokyo) PRMU2021-82
Detecting the position of human hands and an object-in-contact from an image is vital for understanding a user’s actions... [more] PRMU2021-82
pp.139-144
CPSY, DC, IPSJ-SLDM, IPSJ-EMB, IPSJ-ARC [detail] 2022-03-10
10:30
Online Online A Don't Care Filling Method of Control Signals for Concurrent Logical Fault Testing
Haofeng Xu, Toshinori Hosokawa, Hiroshi Yamazaki, Masayuki Arai (Nihon Univ), Masayoshi Yoshimura (KSU) CPSY2021-56 DC2021-90
In recent years, with the increase in test cost for VLSIs, it has been important to reduce the number of test patterns. ... [more] CPSY2021-56 DC2021-90
pp.67-72
SeMI, IPSJ-MBL, IPSJ-UBI 2022-03-08
14:45
Online Online Evaluation of Data Augmentation Methods Considering Occlusion Region for 3D Point Cloud Classification
Shiori Maki, Kenji Kanai, Shota Hirose, Heming Sun, Jiro Katto (Waseda Univ.) SeMI2021-91
In recent years, research of point cloud classification using deep learning has been improved. In this paper, we propose... [more] SeMI2021-91
pp.47-52
PRMU 2021-12-17
10:30
Online Online Data Augmentation to Robust Deep Learning-Based Lesion Classification for CT Image with Different Imaging Conditions
Nobuhiro Miyazaki, Hiroaki Takebe, Takayuki Baba (FUJITSU), Hiroaki Terada, Toru Higaki, Kazuo Awai (Hiroshima Univ.), Masahiko Shimada (Fujitsu Japan) PRMU2021-48
In this paper, we propose a data augmentation to robust DL (deep learning)-based lesion classification for CT image with... [more] PRMU2021-48
pp.130-135
PRMU 2021-10-09
10:45
Online Online Moving Scene Text Detection Using Synthetic Scene Text Video for Training
Zhiyuan Xie, Hideaki Goto, Takuo Suganuma (Tohoku Univ.) PRMU2021-21
In computer vision areas, scene text is valuable information for applications including scene understanding, autopilot, ... [more] PRMU2021-21
pp.28-33
NLC 2021-09-16
10:00
Online Online A causal relation extraction among distant texts using deep learning
Pengju Gao, Tomohiro Yamasaki, Masahiro Ito (TOSHIBA) NLC2021-8
Most of the Existing methods for causal relationship extraction utilize patterns such as clue expressions, but it is dif... [more] NLC2021-8
pp.11-16
PRMU, IPSJ-CVIM, IPSJ-NL 2021-05-21
10:30
Online Online A Study on Domain Adaptation for Video Action Classification Utilizing Synthetic Data.
Hana Isoi (Ochanomizu Univ.), Atsuko Takefusa (NII), Hidemoto Nakada (AIST), Masato Oguchi (Ochanomizu Univ.) PRMU2021-5
The lack of learning data is considered as one of the reasons why the classification accuracies of deep neural networks ... [more] PRMU2021-5
pp.25-30
PRMU, IPSJ-CVIM 2021-03-05
14:10
Online Online A Consideration on Suspicious Object Detection by Mixup and Improved U-Net
Naruki Kanno, Wataru Kameyama, Toshio Sato, Yutaka Katsuyama, Takuro Sato (Waseda Univ.) PRMU2020-90
In this paper, on suspicious object detection by using semantic segmentation, we study the effectiveness of Mixup data a... [more] PRMU2020-90
pp.121-126
IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] 2021-02-18
10:20
Online Online A Note on Improving Performance of Deep Learning-based Distress Detection for Supporting Maintenance of Subway Tunnels -- Accuracy Verification Focusing on Tunnel Wall Characteristics --
Tomoki Haruyama, Keisuke Maeda, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.)
This paper presents the performance improvement of deep learning-based distress detection to support the maintenance of ... [more]
AI 2021-01-27
13:30
Online Online Automatic Short Answer Scoring using Thesaurus-Based Data Augmentation
Hiroyuki Kato (Kyushu Univ.), Tsunenori Ishioka (DNC), Tsunenori Mine (Kyushu Univ) AI2020-15
In the field of natural language processing, the invention of large-scale general-purpose language models such as BERT h... [more] AI2020-15
pp.7-12
PRMU 2020-12-17
11:15
Online Online A Novel Data Augmentation Framework Based on SeqGAN for Sentiment Analysis
Jiawei Luo, Mondher Bouazizi, Tomoaki Ohtsuki (Keio Univ.) PRMU2020-43
Sentiment analysis is an important field in Natural Language Processing (NLP). It can analyze people's sentiment through... [more] PRMU2020-43
pp.30-35
AI, IPSJ-ICS, JSAI-SAI, JSAI-DOCMAS, JSAI-KBS
(Joint)
2020-03-08
17:40
Hokkaido   Proposal of Data Augmentation Methods for Echo Sounder Image Analysis
Min Jie, Soichiro Yokoyama, Tomohisa Yamashita, Hidenori Kawamura (Hokudai) AI2019-54
Data augmentation plays an important role in deep learning. Recently, RandAugment have been proposed as effective augmen... [more] AI2019-54
pp.1-5
 Results 1 - 20 of 44  /  [Next]  
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