Tabular GANs (Generative AI)
Tabular GANs are a type of Generative Adversarial Network (GAN) that are specifically designed for generating synthetic tabular data. Unlike image data, tabular data is typically represented as a matrix of features, where each row represents an instance or observation, and each column represents a feature or attribute.
Tabular GANs use architectures that are more suitable for tabular data, such as Multi-Layer Perceptrons (MLPs) or Convolutional Neural Networks (CNNs) with 1D filters. The generator network takes a random noise vector as input and produces a synthetic tabular dataset as output. The discriminator network tries to distinguish between real and synthetic data by outputting a binary classification score.
The training process of a Tabular GAN involves updating the generator and discriminator networks in an adversarial manner, where the generator tries to produce synthetic data that can fool the discriminator, and the discriminator tries to correctly distinguish between real and synthetic data. The objective of the generator is to minimize the loss of the discriminator on the synthetic data, while the objective of the discriminator is to maximize the loss on the synthetic data and minimize the loss on the real data.
Tabular GANs have several applications, such as generating synthetic datasets for data augmentation, imputing missing values in datasets, and generating data for testing and validation purposes. However, they also have some limitations, such as the risk of generating biased or unrealistic data if the training data is not representative of the true population.
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About the Author:
Dr. Ray Islam is a Data Scientist (AI and ML) and Advisory Specialist Leader at Deloitte, USA. He holds a PhD in Engineering from the University of Maryland, College Park, MD, USA and has worked with major companies like Lockheed Martin and Raytheon, serving clients such as NASA and the US Airforce. Ray also has a MASc in Engineering from Canada, a MSc in International Marketing, and an MBA from, UK. He is also the Editor-in-Chief of the upcoming peer-reviewed International Research Journal of Ethics for AI (INTJEAI), and his research interests include generative AI, augmented reality, XAI, and ethics in AI.
