A detailed tutorial on implementing Conditional Generative Adversarial Networks (CGANs) for generating synthetic tabular data, demonstrated through a practical example using the Adult Income Dataset.
The implementation covers everything from data preprocessing to model architecture, training, and synthetic data generation with specific conditions.
Reasons to Read -- Learn:
how to implement a complete CGAN system in PyTorch, with practical code examples for generating synthetic tabular data conditioned on specific attributes like education level
key differences between traditional GANs and CGANs, including how conditional information is incorporated into both generator and discriminator networks to control the data generation process
detailed data preprocessing techniques for handling mixed categorical and numerical data, including normalization, encoding, and how to transform synthetic data back to its original form
9 min readauthor: Harish Siva Subramanian
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