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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IA, SITE, IPSJ-IOT [detail] | 2024-03-13 13:40 |
Okinawa | Miyakojima City Future Creation Center (Okinawa, Online) (Primary: On-site, Secondary: Online) |
A Steganalysis of Image Steganography using Real Image Denoising Shinnosuke Toguchi, Takamichi Miyata (CIT)  SITE2023-100 IA2023-106 |
Image steganography is a technique for embedding secret messages in images. SteganoGAN, one of the previous methods, use... [more] | SITE2023-100 IA2023-106 pp.195-202 |
| BioX | 2023-10-12 15:25 |
Okinawa | Nobumoto Ohama Memorial Hall (Okinawa) | A Study on Ear Acoustic Personal Authentication System Using Domain Transformation for Numerical Analysis Data Masafumi Morimoto (Kansai Univ.), Shunsuke Kita (ORIST), Yoshinobu Kajikawa (Kansai Univ.) BioX2023-60 |
We propose a new personal authentication system using ear pinna transfer functions. This proposed method is constructe... [more] |
BioX2023-60 pp.12-15 |
| CCS | 2023-03-27 09:20 |
Hokkaido | RUSUTSU RESORT (Hokkaido) | Learning Commutative Vector Field in Latent Space of Deep Generative Model Takehiro Aoshima, Takashi Matsubara (Osaka Univ.) CCS2022-83 |
Deep generative models, such as generative adversarial networks (GANs), are known for generating high-quality images. Ho... [more] | CCS2022-83 pp.113-116 |
| ICTSSL, CAS | 2023-01-27 11:25 |
Tokyo | TBD (Tokyo, Online) (Primary: On-site, Secondary: Online) |
On automatic illustrations generated by GAN networks Aika Honne, Kazuya Ozawa, Hideaki Okazaki (SIT) CAS2022-83 ICTSSL2022-47 |
In this report, we discuss automatic illustrations generated by GAN networks (adversarial generative networks). First, a... [more] | CAS2022-83 ICTSSL2022-47 pp.104-107 |
| IN, CCS (Joint) |
2022-08-05 09:40 |
Hokkaido | Hokkaido University(Centennial Hall) (Hokkaido, Online) (Primary: On-site, Secondary: Online) |
Machine Learning-Based Network Traffic Prediction with Tunable Parameters Kaito Kuriyama, Kohei Watabe (Nagaoka Univ. of Tech.) IN2022-20 |
Network evaluation has become increasingly important in recent years. Network evaluation requires large amounts of traf... [more] |
IN2022-20 pp.27-32 |
| MI | 2022-07-08 17:00 |
Hokkaido | (Hokkaido, Online) (Primary: On-site, Secondary: Online) |
[Short Paper]
Weakly-Supervised Focal Liver Lesion Detection in CT Images He Li, Yutaro Iwamoto (Ritsumeikan Univ.), Xianhua Han (Yamaguchi Univ.), Lanfen Lin, Ruofeng Tong, Hongjie Hu (Zhejiang Univ.), Akira Furukawa (Tokyo Metropolitan Univ.), Shuzo Kanasaki (Koseikai Takeda Hospital), Yen-Wei Chen (Ritsumeikan Univ.) MI2022-40 |
Convolutional neural networks have been widely used for anomaly detection and one of their most common methods is autoen... [more] | MI2022-40 pp.30-33 |
| AI | 2022-07-04 16:50 |
Hokkaido | (Hokkaido, Online) (Primary: On-site, Secondary: Online) |
A generative model for generation of playable levels in 2D video games. Soichiro Takata, Yuichi Sei, Yasuyuki Tahara, Akihiko Ohsuga (UEC) AI2022-16 |
(To be available after the conference date) [more] | AI2022-16 pp.82-87 |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2022-06-28 10:05 |
Okinawa | (Okinawa, Online) (Primary: On-site, Secondary: Online) |
Learning Attribute Vector Fields in GAN Latent Space Takehiro Aoshima, Takashi Matsubara (Osaka Univ.) NC2022-12 IBISML2022-12 |
Generative Adversarial Networks (GANs) can generate a great variety of high-quality images. Despite their ability to g... [more] |
NC2022-12 IBISML2022-12 pp.94-99 |
| SP, IPSJ-MUS, IPSJ-SLP [detail] | 2022-06-17 15:00 |
Online | Online (Online) | SP2022-13 |
We investigate the method for unsupervised learning of artifacts correction networks used for post-processing of Multi B... [more] | SP2022-13 pp.49-54 |
| IMQ | 2022-05-27 14:25 |
Tokyo | (Tokyo) | Classification-ESRGAN
-- Synthesis of super-resolution images based on subject categorization -- Jingan Liu, Atsumu Harada, Naiwala P. Chandrasiri (Kogakuin Univ.) IMQ2022-3 |
In recent years, super-resolution techniques have been significantly developed based on deep learning. In particular, GA... [more] | IMQ2022-3 pp.12-17 |
| IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] | 2022-02-21 12:45 |
Online | 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 |
| MI | 2022-01-26 13:00 |
Online | Online (Online) | Relationship between Image Quality and Learning Effect in Color Laparoscopic Images Generation by Generative Adversarial Networks Norifumi Kawabata (Hokkaido Univ.), Toshiya Nakaguchi (Chiba Univ.) MI2021-59 |
Improving of personal computer performance, it is possible for healthcare workers and related researchers to support for... [more] | MI2021-59 pp.59-64 |
| IBISML | 2022-01-17 10:40 |
Online | 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 |
| IPSJ-AVM, CS, IE, ITE-BCT [detail] | 2021-11-25 10:25 |
Online | 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 |
| MI, MICT [detail] | 2021-11-05 15:50 |
Online | Online (Online) | [Short Paper]
Sketch-based CT image generation of lung cancers using Pix2pix
-- An attempt to improve representation by adopting Style Blocks -- Ryo Toda, Atsushi Teramoto (FHU), Masakazu Tsujimoto (FHUH), Hiroshi Toyama, Masashi Kondo, Kazuyoshi Imaizumi, Kuniaki Saito (FHU), Hiroshi Fujita (Gifu Univ.) MICT2021-42 MI2021-40 |
Generative adversarial networks (GAN) have been used to overcome the lack of data in medical images. However, such appli... [more] | MICT2021-42 MI2021-40 pp.66-67 |
| CAS, NLP | 2021-10-14 15:50 |
Online | Online (Online) | Implementation of a Generative Adversarial Network as Bitwise Neural Network Takuma Matsuno, Gauthier Lovic (Ariake College) CAS2021-28 NLP2021-26 |
Generative Adversarial Network (GAN) is an artificial intelligence algorithm in which a generative network, which produc... [more] | CAS2021-28 NLP2021-26 pp.62-67 |
| PRMU | 2021-10-09 09:00 |
Online | Online (Online) | Omni-Directional Image Representation in GAN-based Image Generator Keisuke Okubo, Takao Yamanaka (Sophia Univ.) PRMU2021-17 |
The omni-directional image generation from a snapshot image taken by an ordinary camera has been developed using conditi... [more] | PRMU2021-17 pp.5-10 |
| CCS | 2021-03-29 16:05 |
Online | Online (Online) | IMAS-GAN: Unsupervised Domain Translation without Cycle Consistency Masashi Okada, Hidehiro Nakano, Arata Miyauchi (Tokyo City Univ.) CCS2020-28 |
CycleGAN realizes the translation between domains without using pair data. However, the configuration of two GANs and th... [more] | CCS2020-28 pp.42-47 |
| MI | 2021-03-17 11:00 |
Online | Online (Online) | Optimal Design and Quality Assessment of Color Laparoscopic Super-Resolution Image by Generative Adversarial Networks Norifumi Kawabata (Tokyo Univ. of Science), Toshiya Nakaguchi (Chiba Univ.) MI2020-91 |
The Generative Adversarial Networks (GAN) is unsupervised learning enabled to transform according to data characteristic... [more] | MI2020-91 pp.186-190 |
| MI | 2021-03-17 13:45 |
Online | Online (Online) | Medical Image Style Translation by Adversarial Training with Paired Inputs Kazuki Fujioka (Kobe Univ.), Takashi Matsubara (Osaka Univ.), Kuniaki Uehara (Osaka Gakuin Univ.) MI2020-96 |
Medical image diagnosis by artificial intelligence requires a large amount of data for learning. However, preparing such... [more] | MI2020-96 pp.212-217 |
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