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One-Class Support Vector Machine Generative Adversarial Network

  • Author / Creator
    Wang, Siting
  • Generative Adversarial Networks (GAN) are a field of popular generating models, and there are many variants these years. Lim and Ye 2017 proposed the Geometric GAN to connect the network with the geometric interpretation. They update the discriminator based on the algorithm of Support Vector Machines (SVM), Inspired by their work, we proposed a new algorithm using the robust One Class Support Vector Machines. We also proposed that the discriminator should separate the dataset into three groups: the correctly classified real data, the correctly classified generated data, and the incorrectly classified data. By eliminating the space of incorrectly classified data, we can have our discriminator capture more patterns. We tested our model and the Geometric GAN on the MNIST dataset, and our model has better performance in the same setting.

  • Subjects / Keywords
  • Graduation date
    Spring 2023
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/r3-012v-jt22
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.