Conditional Data Synthesis Augmentation.

Area of Science:

• Machine Learning
• Data Science
• Artificial Intelligence

Background:

• Reliable machine learning and statistical analysis require diverse, well-distributed training data.
• Real-world datasets often suffer from limited size and underrepresentation of key subpopulations, leading to biased predictions and reduced model performance, especially in supervised learning tasks like classification.

Purpose of the Study:

• To introduce Conditional Data Synthesis Augmentation (CoDSA), a novel framework designed to synthesize high-fidelity data for enhancing model performance.
• To address challenges posed by limited and imbalanced datasets in multimodal domains (tabular, textual, image).

Main Methods:

• Leveraging generative models, specifically diffusion models, to synthesize high-fidelity data.
• Fine-tuning pre-trained generative models via transfer learning to enhance synthetic data realism and increase sample density in sparse areas.
• Developing a theoretical framework to quantify statistical accuracy improvements based on synthetic sample volume and targeted region allocation.

Main Results:

• CoDSA generates synthetic samples that accurately capture conditional distributions, focusing on under-sampled regions.
• The framework preserves inter-modal relationships, mitigates data imbalance, improves domain adaptation, and enhances generalization.
• Extensive experiments show CoDSA consistently outperforms non-adaptive augmentation strategies and state-of-the-art baselines in both supervised and unsupervised settings.

Conclusions:

• CoDSA offers a robust solution for data augmentation, effectively addressing data limitations and improving machine learning model performance.
• The proposed framework provides formal guarantees of effectiveness and demonstrates superior performance across diverse data modalities and learning tasks.