Yang, Jingbo and Bors, Adrian Gheorghe orcid.org/0000-0001-7838-0021 (2022) Encoder Enabled GAN-based Video Generators. In: IEEE International Conference on Image Processing (ICIP). IEEE , Bordeaux, France , pp. 1841-1845.
Abstract
This research study proposes a compatible encoder-enabled video generating method. The encoder-enabled method adds an inference mechanism for enhancing the ability of Generative Adversarial Networks (GAN) based video generators. The proposed video generating method is called Encoding GAN3 (EncGAN3) and decomposes the video into two streams representing content and movement, respectively. The proposed model consists of three processing modules, representing Encoder, Generator and Discriminator, each trained separately, by considering its own loss function. EncGAN3 is shown to generate videos of high quality, according to both visual and numerical results.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 IEEE. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 09 Nov 2022 10:20 |
Last Modified: | 12 Jan 2025 00:12 |
Published Version: | https://doi.org/10.1109/ICIP46576.2022.9897233 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ICIP46576.2022.9897233 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:193131 |