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A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
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His-GAN: A histogram-based GAN model to improve data generation quality.

Wei Li1, Wei Ding2, Rajani Sadasivam3

  • 1School of Cyber Science and Engineering, Wuhan University, China.

Neural Networks : the Official Journal of the International Neural Network Society
|August 4, 2019
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Summary

This study introduces His-GAN, a novel histogram-based Generative Adversarial Network (GAN) for generating high-quality numeric simulation data. His-GAN improves data realism by incorporating histogram dissimilarity into training and processing data in single-group batches.

Keywords:
-GANHistogram-based evaluation metricNumeric simulation data generation

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Data Science

Background:

  • Generative Adversarial Networks (GANs) excel at generating simulation data but struggle with ensuring generated data closely matches real data distributions, especially for numeric datasets.
  • Traditional GANs primarily focus on image generation, presenting challenges for effective application to complex numeric data.

Purpose of the Study:

  • To propose a novel histogram-based Generative Adversarial Network (His-GAN) model designed to enhance the quality and realism of generated numeric data.
  • To address the limitations of standard GANs in accurately capturing the distributions of real-world numeric datasets.

Main Methods:

  • Developed His-GAN, which maps generated and original data to histograms, calculating dissimilarity using probability percentiles and metrics like Hellinger distance and Jensen-Shannon divergence.
  • Integrated this dissimilarity score into the GAN training process to refine generator parameters and improve data quality.
  • Revised the GAN training by processing data in single-group batches to capture specific data characteristics and avoid issues with mixed data clusters.

Main Results:

  • Extensive experiments on MNIST, CIFAR-10, and a real-world numeric dataset demonstrated the effectiveness of the His-GAN approach.
  • The His-GAN model successfully generated data that is more indistinguishable from original data distributions.
  • The histogram-based dissimilarity metric and single-group batch processing significantly improved generated data quality.

Conclusions:

  • His-GAN offers a robust method for improving the quality of generated data from Generative Adversarial Networks, particularly for numeric datasets.
  • The proposed techniques enhance the ability of GANs to produce realistic simulation data by better aligning generated and original data distributions.
  • His-GAN represents a significant advancement in applying GANs to complex numeric data generation tasks.