<ul><li>Conditional Data Synthesis Augmentation (CoDSA) is a framework that uses generative models to synthesize diverse and well-distributed data for improving machine learning and statistical analysis.</li><li>CoDSA focuses on addressing data limitations and biases by generating synthetic samples that capture the conditional distributions of the original data, particularly in under-sampled or high-interest regions.</li><li>CoDSA leverages transfer learning to enhance the realism of synthetic data and increase sample density in sparse areas, preserving inter-modal relationships and improving domain adaptation and generalization of models.</li><li>Experiments indicate that CoDSA consistently outperforms non-adaptive augmentation strategies and state-of-the-art baselines in both supervised and unsupervised settings.</li></ul>

Conditional Data Synthesis Augmentation

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