Proceedings of the 2023 2nd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2023)

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Data Generation and Latent Space Based Feature Transfer Using ED-VAEGAN, an Improved Encoder and Decoder Loss VAEGAN Network

Authors
Jiatong Li1, 2, *
1The Grainger College of Engineering, Electrical and Computer Engineering, Zhejiang University, Hangzhou, China
2University of Illinois at Urbana Champaign, Illinois, USA
*Corresponding author. Email: jl180@illinois.edu
Corresponding Author
Jiatong Li
Available Online 28 August 2023.
DOI
10.2991/978-94-6463-222-4_12How to use a DOI?
Keywords
ED-VAEGAN; Feature-wise Reconstruction loss; latent space expedition
Abstract

To combine the advantages of VAEs and GANs to generate both diverse and high-quality samples, this paper proposes ED-VAEGAN which improves encoder and decoder loss of traditional feature-wise VAEGAN [4]. More precisely, a reconstruction score term is added to encoder loss function, which accelerates the training of the whole model. The decoder loss was similar to traditional definition, but discarded an irrelevant term to decoder. This paper applied this new model to face datasets and compares the generations with other models when the models are fully trained and when trained for the same iterations. And the latent space expedition was done by first encode the images and then do the latent code walk between two images. As a result, ED-VAEGAN outperformed traditional VAEGAN on training speed, and its latent space expedition result indicates better continuity comparing to other pixel-wise models. In the end, this paper applied simple data augmentation method to solve the brightness problem that happened when training iterations increase.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 2nd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2023)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
28 August 2023
ISBN
10.2991/978-94-6463-222-4_12
ISSN
2589-4919
DOI
10.2991/978-94-6463-222-4_12How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Jiatong Li
PY  - 2023
DA  - 2023/08/28
TI  - Data Generation and Latent Space Based Feature Transfer Using ED-VAEGAN, an Improved Encoder and Decoder Loss VAEGAN Network
BT  - Proceedings of the 2023 2nd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2023)
PB  - Atlantis Press
SP  - 123
EP  - 135
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-222-4_12
DO  - 10.2991/978-94-6463-222-4_12
ID  - Li2023
ER  -