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Updated: Jan 19, 2026

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Generative adversarial network based on chaotic time series.

Makoto Naruse1,2, Takashi Matsubara3, Nicolas Chauvet4

  • 1Department of Information Physics and Computing, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan. makoto_naruse@ipc.i.u-tokyo.ac.jp.

Scientific Reports
|September 12, 2019
PubMed
Summary
This summary is machine-generated.

This study uses chaotic time series from semiconductor lasers as input for Generative Adversarial Networks (GANs). This approach enhances image robustness while maintaining versatility, offering new possibilities for AI-driven image generation.

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Area of Science:

  • Artificial Intelligence
  • Nonlinear Dynamics
  • Optoelectronics

Background:

  • Generative Adversarial Networks (GANs) are powerful tools for realistic image synthesis.
  • GANs typically require large datasets and pseudorandom numbers for training.
  • The use of chaotic dynamics in GANs remains an underexplored area.

Purpose of the Study:

  • To investigate the impact of experimentally generated chaotic time series on GANs.
  • To explore the potential of chaotic systems as latent variables for image generation.
  • To analyze the effects on generated image properties, such as robustness and versatility.

Main Methods:

  • Utilized chaotic time series from semiconductor lasers as latent variables for a GAN.
  • Employed deep convolutional neural networks and adversarial training mechanisms.
  • Analyzed image properties including similarity in proximity and autocorrelation signatures.

Main Results:

  • Enhanced similarity in proximity of generated images, indicating improved robustness to input variations.
  • Maintained overall versatility of the generated images without significant degradation.
  • Demonstrated that surrogate chaos time series can eliminate specific signatures in generated images.

Conclusions:

  • Chaotic time series can be effectively integrated into GANs to influence generated image characteristics.
  • This novel approach offers a method to enhance image robustness in generative models.
  • The findings open avenues for exploring complex dynamics in artificial intelligence applications.