Frame difference generative adversarial networks: Clearer contour video generating

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

Generating image and video is a hot topic in Deep Learning. Especially, generating video is a difficult but meaningful work. How to generate video which has diversity and plausibility is still a problem to be solved. In this paper, we propose a novel model of Generative Adversarial Network(GAN) which called FDGAN to generate clear contour lines. Unlike existing GAN that only use frames, our method extends to use inter-frame difference. First introduce two temporal difference methods to process the inter-frame. Then increase a frame difference discriminator to discriminate whether the inter-frame is true or not. Using the model and new structure proposed, we perform video generation experiments on several widely used benchmark datasets such as MOVING MNIST, UCF-101. Consequently, the results achieve state-of-the-art performance for clarifying contour lines. Both quantitative and qualitative evaluations were made to show the effectiveness of our methods.

本文言語英語
ホスト出版物のタイトルProceedings - 2019 7th International Symposium on Computing and Networking Workshops, CANDARW 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ169-175
ページ数7
ISBN(電子版)9781728152684
DOI
出版ステータス出版済み - 11 2019
イベント7th International Symposium on Computing and Networking Workshops, CANDARW 2019 - Nagasaki, 日本
継続期間: 11 26 201911 29 2019

出版物シリーズ

名前Proceedings - 2019 7th International Symposium on Computing and Networking Workshops, CANDARW 2019

会議

会議7th International Symposium on Computing and Networking Workshops, CANDARW 2019
国/地域日本
CityNagasaki
Period11/26/1911/29/19

All Science Journal Classification (ASJC) codes

  • ハードウェアとアーキテクチャ
  • 情報システム
  • 人工知能
  • コンピュータ ネットワークおよび通信

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