#tabular
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· Conditional Tabular GAN - CTGAN
One of the most interesting ideas on the last decades in machine learning is the GAN architecture for generatine model. While GANs have shown remarkable success in generating high-quality images and other continuous data types, tabular data poses unique challenges. Tabular data often contains a mix of discrete and continuous variables, with complex dependencies between them. Traditional GAN architectures struggle to capture these intricacies, leading to poor performance when applied directly to tabular data. CTGAN addresses these challenges by introducing a specialized framework for generating realistic synthetic tabular data.