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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 51  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
IN, CCS
(Joint)
2022-08-05
09:40
Hokkaido Hokkaido University(Centennial Hall)
(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
(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
(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
(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 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   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 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 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 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 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 [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 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 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 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 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 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
EMM 2021-03-04
14:45
Online Online [Poster Presentation] Improvement of Video Forgery Detection Using Generative Adversarial Networks
Yutaro Osako (Osaka Univ.), Kazuhiro Kono (Kansai Univ.), Noboru Babaguchi (Osaka Univ.) EMM2020-72
Our work aims to detect tampered objects in the spatial domain of videos with high accuracy. We target videos, including... [more] EMM2020-72
pp.28-33
IE 2021-01-21
14:45
Online Online [Invited Talk] GAN-based Image Coding Methods for Maximizing Subjective Image Quality
Shinobu Kudo (NTT) IE2020-37
The increasing image resolution and the spread of IoT devices require more efficient video storage and transmission syst... [more] IE2020-37
pp.9-13
MBE, NC
(Joint)
2020-12-18
14:50
Online Online Super resolution for sea surface temperature with CNN and GAN
Tomoki Izumi, Motoki Amagasaki, Kei Ishida, Masato Kiyama (Kumamoto Univ.) NC2020-28
In this paper, we use the deep neural networks (DNN)-based single image super-resolution (SISR) method for the super res... [more] NC2020-28
pp.1-6
PRMU 2020-09-02
15:45
Online Online Collaborative learning for generative adversarial networks
Takuya Tsukahara, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi (Chubu Univ.) PRMU2020-14
Generative adversarial networks (GANs) adversarially trains generative and discriminative models. And this is how to gen... [more] PRMU2020-14
pp.41-46
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