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Generalization error property of infoGAN for two-layer neural network

arXiv 2310.00443 ↗First posted 30 Sep 2023stat.ML cs.LGcited by 0

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Abstract (arXiv)

Information Maximizing Generative Adversarial Network (infoGAN) can be understood as a minimax problem involving two neural networks: discriminators and generators with mutual information functions. The infoGAN incorporates various components, including latent variables, mutual information, and objective function. This research demonstrates the Generalization error property of infoGAN as the discriminator and generator sample size approaches infinity. This research explores the generalization error property of InfoGAN as the sample sizes of the discriminator and generator approach infinity. To establish this property, the study considers the difference between the empirical and population versions of the objective function. The error bound is derived from the Rademacher complexity of the discriminator and generator function classes. Additionally, the bound is proven for a two-layer network, where both the discriminator and generator utilize Lipschitz and non-decreasing activation functions.

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References · 15

  1. 2014Conditional Generative Adversarial Netscited by 2,162
  2. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
  3. 2016InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Netscited by 860
  4. 2016Generative Adversarial Text to Image Synthesiscited by 735
  5. 2015Generating Sentences from a Continuous Spacecited by 612
  6. 2016f-GAN: Training Generative Neural Samplers using Variational Divergence Minimizationcited by 506
  7. 2015Generative Moment Matching Networkscited by 257
  8. 2018Generative Adversarial Network in Medical Imaging: A Reviewcited by 257
  9. 2020A Review on Generative Adversarial Networks: Algorithms, Theory, and Applicationscited by 209
  10. 2017Generalization and Equilibrium in Generative Adversarial Nets (GANs)cited by 166
  11. 2018How Well Generative Adversarial Networks Learn Distributionscited by 78
  12. 2018Learning Plannable Representations with Causal InfoGANcited by 57
3 more
  1. 2021An error analysis of generative adversarial networks for learning distributionscited by 36
  2. 2018Nonparametric Density Estimation under Adversarial Lossescited by 26
  3. 2019LOGAN: Unpaired Shape Transform in Latent Overcomplete Spacecited by 8

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