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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 249  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
NC, MBE
(Joint)
2024-03-12
13:55
Tokyo The Univ. of Tokyo
(Primary: On-site, Secondary: Online)
Identification of filamentous fungi by segmentation models using consistency regularization and classmix
Taiga Shimizu (Yamanashi Univ.), Waleed Asghar (Oklahoma State Univ.), Ryota Kataoka, Motonobu Hattori (Yamanashi Univ.) NC2023-57
In agriculture, soil diagnosis is necessary to protect the environment. However, since current diagnostic methods are no... [more] NC2023-57
pp.81-86
MI 2024-03-03
17:18
Okinawa OKINAWAKEN SEINENKAIKAN
(Primary: On-site, Secondary: Online)
3D shape reconstruction of colon with model-based unsupervised depth estimation
Natsu Onozaka (Nagoya Univ.), Hayato Itoh (Fukuoka Univ.), Masahiro Oda (Nagoya Univ.), Masashi Misawa (Showa Univ.), Yuichi Mori (UiO), Shin-ei Kudo (Showa Univ.), Kensaku Mori (Nagoya Univ.) MI2023-60
We propose unsupervised trainig for the pose estimation in 3D reconstrcution of the colon from colonoscopic images by cl... [more] MI2023-60
pp.87-90
MI 2024-03-04
09:00
Okinawa OKINAWAKEN SEINENKAIKAN
(Primary: On-site, Secondary: Online)
Distance-informed adversarial learning for metal artifact reduction
Daisuke Shigemori, Megumi Nakao (Kyoto Univ.) MI2023-62
In this study, we propose an adversarial learning framework that utilises distance information from metal to reduce CT m... [more] MI2023-62
pp.95-98
MI 2024-03-04
15:58
Okinawa OKINAWAKEN SEINENKAIKAN
(Primary: On-site, Secondary: Online)
[Short Paper] Application of representations obtained by self-supervised learning of hierarchical ViT to discriminate between good and bad breast tumors.
Akiomi Ishikawa (NIT), Kouji Arihiro (HIrosima Univ), Hidekata Hontani (NIT) MI2023-88
In this paper, I report a method to apply the representation of pathological microscopic images obtained by self-supervi... [more] MI2023-88
pp.184-185
NS, IN
(Joint)
2024-02-29
11:10
Okinawa Okinawa Convention Center An unsupervised online learning-based traffic classification and anomaly detection method for 5G-IIoT systems
Yuxuan Shi, Qianqian Pan, Akihiro Nakao (U Tokyo) NS2023-188
In the context of Society 5.0, the evolution of the Internet of Things (IoT) and its ever growing demands of massive Mac... [more] NS2023-188
pp.96-102
DE, IPSJ-DBS 2023-12-26
14:00
Tokyo Institute of Industrial Science, The University of Tokyo Interpretation of unsupervised clustering based on XAI
Yu Sasaki, Fumiaki Saitoh (CIT) DE2023-28
Explainable Artificial Intelligence (XAI) aims to introduce transparency and interpretability into the decision-making o... [more] DE2023-28
pp.1-6
QIT
(2nd)
2023-12-18
14:30
Okinawa OIST
(Primary: On-site, Secondary: Online)
Advantage of Quantum Machine Learning from General Computational Advantages
Hayata Yamasaki, Natsuto Isogai, Mio Murao (UTokyo)
Demonstrating the existence of general learning problems where machine learning using quantum computers exhibits rigorou... [more]
WIT, HI-SIGACI 2023-12-07
11:15
Tokyo AIST Tokyo Waterfront (TBD) On Modeling Daily Activities of the Elderly Living Alone Using Markov Series of Dirichlet Multinomial Mixture Models
Ken Sadohara (AIST) WIT2023-30
To develop smart home technology designed to analyze the activity of residents based on the logs of installed sensors, a... [more] WIT2023-30
pp.31-36
SP, NLC, IPSJ-SLP, IPSJ-NL [detail] 2023-12-03
11:05
Tokyo Kikai-Shinko-Kaikan Bldg.
(Primary: On-site, Secondary: Online)
[Poster Presentation] Self-supervised learning model based emotion transfer and intensity control technology for expressive speech synthesis
Wei Li, Nobuaki Minematsu, Daisuke Saito (Univ. of Tokyo) NLC2023-21 SP2023-41
Emotion transfer techniques, which transfersba the speaking style from the reference speech to the target speech, are wi... [more] NLC2023-21 SP2023-41
pp.43-48
MI, MICT 2023-11-14
15:00
Fukuoka   Pre-training without natural images for Cystoscopic AI Diagnosis of Bladder Cancer
Ryuunosuke Kounosu (AIST/Toho Univ.), Wonjik Kim (AIST), Atsushi Ikeda (Univ. of Tsukuba), Hirokazu Nosato (AIST), Yuu Nakajima (Toho Univ.) MICT2023-34 MI2023-27
When developing AI models, it is sometimes difficult to collect sufficient training data. In these cases, pre-trained AI... [more] MICT2023-34 MI2023-27
pp.37-40
BioX 2023-10-13
10:20
Okinawa Nobumoto Ohama Memorial Hall Discrimination between Real and Generated Gestures of Speakers -- An Attempt to Improve Generalization Performance in Unseen Generation Methods through Self-Supervised Learning --
Geng Mu (AGU), Naoshi Kaneko (TDU), Kazuhiko Sumi (AGU) BioX2023-67
Currently, discerning artificially generated misinformation is a critical societal challenge, with research progressing ... [more] BioX2023-67
pp.44-49
IA 2023-09-22
10:40
Hokkaido Hokkaido Univeristy
(Primary: On-site, Secondary: Online)
OLIViS: An OSINT-Based Lightweight Method for Identifying Video Content Services for Capacity Planning in Backbone ISPs
Yuki Tamura, Fumio Teraoka, Takao Kondo (Keio Univ.) IA2023-23
As of 2022, 66% of Internet traffic is generated by video content services, among which Netflix and YouTube are the domi... [more] IA2023-23
pp.75-82
IBISML 2023-09-08 Osaka Osaka Metropolitan University (Nakamozu Campus)
(Primary: On-site, Secondary: Online)
Proposal of a Learning Time Reduction Algorithm in Machine Learning through Input Data Abstraction
Tsubasa Sakoda IBISML2023-27
In this research, I attempt to reduce the learning time of machine learning by using simple calculation such as averagin... [more] IBISML2023-27
pp.12-15
IBISML 2023-09-08
13:25
Osaka Osaka Metropolitan University (Nakamozu Campus)
(Primary: On-site, Secondary: Online)
Consideration of Negative Samples in Contrastive Learning
Daiki Ishiguro, Tomoko Ozeki (Tokai Univ.) IBISML2023-28
Contrastive learning has achieved accuracy comparable to supervised learning. In this method, the transformed image pair... [more] IBISML2023-28
pp.16-21
CQ, MIKA
(Joint)
(2nd)
2023-08-31
10:50
Fukushima Tenjin-Misaki Sports Park [Poster Presentation] Study on the Effectiveness of Building TCP Throughput Prediction Model using Federated Learning
Han Nay Aung, Hiroyuki Ohsaki (Kwansei Gakuin Univ)
In the realm of communication networks, ensuring accurate forecasts for the performance of TCP flows is essential to ach... [more]
PRMU, IPSJ-CVIM 2023-05-19
15:40
Aichi
(Primary: On-site, Secondary: Online)
Object-Centric Representation Learning with Attention Mechanism
Hidemoto Nakada, Hideki Asoh (AIST) PRMU2023-13
For object-centric representation learning, several slot-based methods, that separate objects using masks and learn the ... [more] PRMU2023-13
pp.68-73
NLP, MSS 2023-03-17
16:05
Nagasaki
(Primary: On-site, Secondary: Online)
Lightweighting Noisy Student Semi-Supervised Learning by Applying MobileNet
Yuga Morishima, Hidehiro Nakano (Tokyo City Univ.) MSS2022-108 NLP2022-153
Recently, Convolutional Neural Networks (CNNs) have attracted much attention in various fields such as image classificat... [more] MSS2022-108 NLP2022-153
pp.220-224
MI 2023-03-06
17:04
Okinawa OKINAWA SEINENKAIKAN
(Primary: On-site, Secondary: Online)
[Short Paper] Rotation-Equivariant CNN for Medical Image Processing Applications
Ryota Ogino, Kugler Mauricio, Tatsuya Yokota, Hidekata Hontani (NITech) MI2022-96
In this study, we report an attempt to use a Rotation-Equivariant CNN to organize image data whose rotation direction an... [more] MI2022-96
pp.113-114
MI 2023-03-07
15:38
Okinawa OKINAWA SEINENKAIKAN
(Primary: On-site, Secondary: Online)
[Short Paper] A Denoising Method for Low Dose CT by Iterative Processing Using Self-Supervised Learning
Yuki Sato, Hiroyuki Kudo (Univ of Tsukuba) MI2022-121
In recent years, patient exposure has become an issue, and low-dose CT, which reduces the amount of radiation irradiated... [more] MI2022-121
pp.192-193
PRMU, IBISML, IPSJ-CVIM [detail] 2023-03-02
11:05
Hokkaido Future University Hakodate
(Primary: On-site, Secondary: Online)
On the Effectiveness of Formula-Driven Supervised Learning for Medical Image Tasks
Ryuto Endo, Shuya Takahashi, Eisaku Maeda (TDU) PRMU2022-71 IBISML2022-78
Deep learning for image information processing often uses manually maintained natural image data. However, these data ha... [more] PRMU2022-71 IBISML2022-78
pp.71-75
 Results 1 - 20 of 249  /  [Next]  
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