CTGAN Explained
A technical deep-dive into CTGAN: the conditional tabular GAN architecture, how it handles mixed data types and imbalanced columns, and when to use it.
CTGAN (Conditional Tabular GAN) is a generative adversarial network designed specifically for tabular (structured) data — the most common format in enterprise AI and analytics.
Standard GAN architectures perform poorly on tabular data because tabular datasets contain mixed types (continuous and discrete), imbalanced categorical columns, and multi-modal distributions that image-focused architectures are not designed to handle.
CTGAN addresses these challenges through two innovations: mode-specific normalization for continuous columns, and a conditional generator that learns to reproduce the marginal distribution of categorical features.
Architecture Overview
CTGAN uses a conditional generator that takes as input both random noise and a one-hot encoding of a randomly sampled categorical value. The discriminator receives real and synthetic rows along with their conditional vectors. This forces the generator to learn to produce realistic samples conditioned on specific categorical values, improving coverage of rare categories.
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