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Yanxiang Gong1, Feiyang Sun2, Xin Ma1

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This study introduces covariance constraints to generative adversarial networks to suppress mode collapse in multivariate data. The novel approach enhances data distribution fitting and improves image generation by considering pixel distances.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computer Vision

Background:

  • Mode collapse is a significant challenge in generative adversarial networks (GANs).
  • Existing methods often rely on regularization or specific network modules, limiting compatibility.
  • Multivariate data presents unique challenges for distribution fitting in GANs.

Purpose of the Study:

  • To propose and evaluate novel methods for suppressing mode collapse in GANs for multivariate data.
  • To enhance the distribution fitting approach by incorporating covariance constraints.
  • To adapt these methods for image generation tasks, improving robustness to pixel variations.

Main Methods:

  • Utilizing distribution fitting as the core methodology.
  • Incorporating covariance constraints to enforce linear correlations among variables.
  • Employing difference matrices for image data to consider pixel distances and offsets.

Main Results:

  • The proposed covariance constraints effectively mitigate nonuniform sampling issues in multivariate data.
  • The image-specific scheme demonstrates improved handling of pixel distances and tolerance for offsets.
  • Experiments confirm the effectiveness and competitive performance of the developed methods.

Conclusions:

  • The novel approach successfully suppresses mode collapse by enhancing distribution fitting with covariance constraints.
  • The method offers improved compatibility and practical applicability by avoiding reliance on complex regularization or network modules.
  • The technique shows promise for generating higher-quality multivariate data and images.