On the Reliability of Generative Augmentation: A Wasserstein-Based Theoretical and Empirical Study

arXiv:2609.01410 · stat.ML, cs.LG · Submitted 2026-09-01 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-09-01

Updated: 2026-09-01

Code: https://github.com/yourusername/generative-augmentation-reliability

Terminology

• Fidelity — 58× in this paper · explained in 2 episodes
• Wasserstein Distance — 15× in this paper · explained in 4 episodes
• Generalization — 14× in this paper · explained in 13 episodes
• Data Augmentation — 12× in this paper · explained in 7 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• F1 Score — 8× in this paper · explained in 3 episodes
• Generative Model — 8× in this paper · explained in 2 episodes
• Generative Models — 8× in this paper · explained in 2 episodes
• Optimal Transport — 8× in this paper · explained in 5 episodes
• Random Forest — 6× in this paper
• Consistency — 4× in this paper · explained in 3 episodes
• Generative Adversarial Networks — 4× in this paper
• Adam — 3× in this paper · explained in 2 episodes
• Adversarial Training — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Theoretical Guarantees — 3× in this paper · explained in 2 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Adversarial Model — 1× in this paper · explained in 1 episode
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Generalization Bounds — 1× in this paper · explained in 2 episodes
• Generative Adversarial Networks (GANs) — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Mode Collapse — 1× in this paper · explained in 2 episodes
• Multi-Layer Perceptron — 1× in this paper
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes

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