Reimagining GANs: Bridging Statistics and Variance Regularization

Author(s): Shenggang Li

Originally published on Towards AI.

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From Logistic Regression to Variance-Regularized GANs: Enhancing Generative Modeling for Tabular Data

Generative Adversarial Networks (GANs) have revolutionized AI, enabling applications like image generation, style transfer, and data synthesis. At their core, GANs feature a Generator that creates data and a Discriminator that distinguishes real from fake — a dynamic “game” where both improve.

For newcomers, GANs can feel abstract with their complex architectures and loss functions. This paper simplifies things by starting with tabular data and traditional models, like logistic regression, making GANs easier to grasp.

We also introduce Variance-Regularized GANs (VR-GANs) to improve statistical alignment in tabular data, ensuring generated data better matches real-world distributions. On top of that, we explore Reason Code GANs, enhancing Discriminators to provide insights into how data evolves during generation. This pushes GANs beyond just generating data — they become tools for interpretation and decision-making.

Let’s dive into how these ideas expand what GANs can do!

Let’s break down GANs using a traditional statistical perspective and data science concepts. By leveraging familiar tools like logistic regression and tabular data, we’ll make the concept more approachable. To illustrate, we’ll walk through a practical example:… Read the full blog for free on Medium.

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Published via Towards AI