skip to main content
10.1145/3590003.3590080acmotherconferencesArticle/Chapter ViewAbstractPublication PagescacmlConference Proceedingsconference-collections
research-article

Image Generation Model Applying PCA on Latent Space

Published: 29 May 2023 Publication History

Abstract

Image generation is an important area of artificial intelligence that involves creating new images from existing datasets. It involves learning the distribution of target images from randomly generated vectors. Like other deep learning models, the image generation model requires a vast refined data set to produce high-quality results. When there is little data, there is a problem that the diversity and quality of generated images are compromised. In this paper, we propose a new generative model that applies PCA to the generator of the least square error adversarial generative network that, in turn, generates high-quality images even with a small data set. Unlike the existing models that generate target data from randomly generated noise, in the proposed method the direction of the image to be generated is guided by extracting the features of the target data through PCA. The results section shows the superior performance of the proposed model against a different number of images in datasets.

References

[1]
Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of big data, 6(1), 1-48.
[2]
Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley interdisciplinary reviews: computational statistics, 2(4), 433-459.
[3]
Liu, Z., Luo, P., Wang, X., & Tang, X. (2018). Large-scale celebfaces attributes (celeba) dataset. Retrieved August, 15(2018), 11.
[4]
Bianco, S. (2017). Large age-gap face verification by feature injection in deep networks. Pattern Recognition Letters, 90, 36-42.
[5]
Mao, X., Li, Q., Xie, H., Lau, R. Y., Wang, Z., & Paul Smolley, S. (2017). Least squares generative adversarial networks. In Proceedings of the IEEE international conference on computer vision (pp. 2794-2802).
[6]
Allen, D. M. (1971). Mean square error of prediction as a criterion for selecting variables. Technometrics, 13(3), 469-475.
[7]
Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434.
[8]
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63(11), 139-144.
[9]
Barratt, S., & Sharma, R. (2018). A note on the inception score. arXiv preprint arXiv:1801.01973.
[10]
Turk, M., & Pentland, A. (1991). Eigenfaces for recognition. Journal of cognitive neuroscience, 3(1), 71-86.
[11]
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
[12]
Cha, G. S., Asim, U., Song, M. K., Niaz, A., & Choi, K. N. (2022, August). Image Generation Network Model based on Principal Component Analysis. In 2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE) (pp. 76-80). IEEE.

Cited By

View all

Index Terms

  1. Image Generation Model Applying PCA on Latent Space
      Index terms have been assigned to the content through auto-classification.

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Other conferences
      CACML '23: Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning
      March 2023
      598 pages
      ISBN:9781450399449
      DOI:10.1145/3590003
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 29 May 2023

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. Generative Adversarial Network
      2. Least Square Error
      3. Principal Component Analysis

      Qualifiers

      • Research-article
      • Research
      • Refereed limited

      Conference

      CACML 2023

      Acceptance Rates

      CACML '23 Paper Acceptance Rate 93 of 241 submissions, 39%;
      Overall Acceptance Rate 93 of 241 submissions, 39%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • 0
        Total Citations
      • 37
        Total Downloads
      • Downloads (Last 12 months)17
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 20 Jan 2025

      Other Metrics

      Citations

      Cited By

      View all

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format.

      HTML Format

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media