A Conditional GAN for Tabular Data Generation with Probabilistic Sampling of Latent Subspaces

arXiv:2508.00472 · cs.LG · Submitted 2025-08-01 · Read on arXiv

cs.LG

Submitted: 2025-08-01

Updated: 2026-08-30

Code: https://github.com/lakritidis/ctdGAN

Terminology

• Fidelity — 27× in this paper · explained in 2 episodes
• Clustering — 19× in this paper · explained in 2 episodes
• Generative Model — 15× in this paper · explained in 2 episodes
• Loss Function — 15× in this paper · explained in 2 episodes
• Accuracy — 11× in this paper · explained in 1 episode
• Copula — 10× in this paper · explained in 2 episodes
• Generative Models — 10× in this paper · explained in 2 episodes
• Generative Adversarial Network — 9× in this paper
• Latent Space — 8× in this paper · explained in 7 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Ablation study — 3× in this paper · explained in 1 episode
• Activation Function — 3× in this paper · explained in 1 episode
• Mode Collapse — 3× in this paper · explained in 2 episodes
• Random Forest — 3× in this paper
• Adam — 2× in this paper · explained in 2 episodes
• Covariate Shift — 2× in this paper · explained in 2 episodes
• Differentially Private — 2× in this paper · explained in 3 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Information Leakage — 2× in this paper · explained in 2 episodes
• Nearest Neighbour Distance Ratio — 2× in this paper
• Benchmark — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Differential Privacy — 1× in this paper · explained in 4 episodes
• Distance to the Nearest Neighbour — 1× in this paper
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Adversarial Networks (GANs) — 1× in this paper · explained in 3 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Mixed Column Correlation Difference — 1× in this paper
• Objective Function — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes
• Variational Gaussian Mixture Model — 1× in this paper

Sources

• Auto-Encoding Variational Bayes
• Conditional Generative Adversarial Nets
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Differentially Private Generative Adversarial Network
• Categorical Reparameterization with Gumbel-Softmax
• Copula Flows for Synthetic Data Generation

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