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A Wasserstein perspective of Vanilla GANs.

Lea Kunkel1, Mathias Trabs1

  • 1Karlsruhe Institute of Technology, Department of Mathematics, Germany.

Neural Networks : the Official Journal of the International Neural Network Society
|October 11, 2024
PubMed
Summary

This study connects Vanilla Generative Adversarial Networks (GANs) to Wasserstein distance, extending theoretical results. This allows for improved convergence rates for GANs in estimating probability distributions.

Keywords:
Distribution estimationGenerative adversarial networksOracle inequalityRate of convergenceWasserstein distance

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Area of Science:

  • Machine Learning
  • Statistical Theory
  • Deep Learning

Background:

  • Generative Adversarial Networks (GANs) have shown empirical success, driving theoretical research.
  • Current statistical research primarily focuses on Wasserstein GANs, with limited results for Vanilla GANs.
  • Vanilla GAN theory often requires restrictive assumptions like smooth activations and equal latent/ambient dimensions.

Purpose of the Study:

  • To bridge the theoretical gap in understanding Vanilla GANs.
  • To extend existing Wasserstein GAN results to the Vanilla GAN framework.
  • To establish convergence rates for GANs as probability distribution estimators.

Main Methods:

  • Establishing a theoretical connection between Vanilla GANs and Wasserstein distance.
  • Developing an oracle inequality for Vanilla GANs in the Wasserstein distance framework.
  • Analyzing feedforward ReLU networks for approximating Lipschitz functions.

Main Results:

  • Existing Wasserstein GAN results are extended to Vanilla GANs.
  • An oracle inequality for Vanilla GANs in Wasserstein distance is derived.
  • Assumptions for the oracle inequality are compatible with common network architectures like feedforward ReLU networks.

Conclusions:

  • A quantitative result for approximating Lipschitz functions using feedforward ReLU networks is provided.
  • Convergence rates for both Vanilla GANs and Wasserstein GANs as probability distribution estimators are established.
  • The findings facilitate broader theoretical analysis of practical GAN implementations.