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Authors: Takuya Tsukahara ; Tsubasa Hirakawa ; Takayoshi Yamashita and Hironobu Fujiyoshi

Affiliation: Chubu University, 1200 Matsumoto-cho, Kasugai, Aichi, Japan

Keyword(s): Generative Adversarial Networks, Deep Mutual Learning, Deep Learning, Convolutional Neural Network.

Abstract: Generative adversarial networks (GANs) adversarially train generative and discriminative and generate a nonexistent images. Common GANs use only a single generative model and discriminant model and are considered to maximize their performance. On the other hand, in the image-classification task, recognition accuracy improves by collaborative learning in which knowledge transfer is conducted among several neural networks. Therefore, we propose a method that involves using GANs with multiple generative models and one discriminant model to conduct collaborative learning while transferring information among the generative models. We conducted experiments to evaluate the proposed method, and the results indicate that the quality of the images produced by the proposed method is improved and increased in diversity.

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Paper citation in several formats:
Tsukahara, T.; Hirakawa, T.; Yamashita, T. and Fujiyoshi, H. (2021). Collaborative Learning of Generative Adversarial Networks. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP; ISBN 978-989-758-488-6; ISSN 2184-4321, SciTePress, pages 492-499. DOI: 10.5220/0010251804920499

@conference{visapp21,
author={Takuya Tsukahara. and Tsubasa Hirakawa. and Takayoshi Yamashita. and Hironobu Fujiyoshi.},
title={Collaborative Learning of Generative Adversarial Networks},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP},
year={2021},
pages={492-499},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010251804920499},
isbn={978-989-758-488-6},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP
TI - Collaborative Learning of Generative Adversarial Networks
SN - 978-989-758-488-6
IS - 2184-4321
AU - Tsukahara, T.
AU - Hirakawa, T.
AU - Yamashita, T.
AU - Fujiyoshi, H.
PY - 2021
SP - 492
EP - 499
DO - 10.5220/0010251804920499
PB - SciTePress

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