Conditional Data Synthesis Augmentation

2025-04-10

AI Summary

Real-world data often suffer from limited scale and imbalanced subgroup coverage, leading to poor generalization and high bias in classification tasks. To address this, we propose a conditional multimodal data synthesis framework for tabular, textual, and visual modalities, generating high-fidelity, conditionally faithful synthetic samples. Our contributions are threefold: (1) a conditional-distribution-focused synthesis strategy that enables density-adaptive augmentation in sparse regions—first of its kind; (2) a theoretically grounded framework with provable statistical gains, providing rigorous error bounds; and (3) a cross-modal relational preservation mechanism integrating diffusion model fine-tuning, conditional generative modeling, and multimodal alignment. Experiments demonstrate that our method significantly outperforms non-adaptive augmentation and state-of-the-art baselines across both supervised and unsupervised tasks, effectively mitigating data imbalance while improving classification accuracy and domain adaptation performance.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Reliable machine learning and statistical analysis rely on diverse, well-distributed training data. However, real-world datasets are often limited in size and exhibit underrepresentation across key subpopulations, leading to biased predictions and reduced performance, particularly in supervised tasks such as classification. To address these challenges, we propose Conditional Data Synthesis Augmentation (CoDSA), a novel framework that leverages generative models, such as diffusion models, to synthesize high-fidelity data for improving model performance across multimodal domains including tabular, textual, and image data. CoDSA generates synthetic samples that faithfully capture the conditional distributions of the original data, with a focus on under-sampled or high-interest regions. Through transfer learning, CoDSA fine-tunes pre-trained generative models to enhance the realism of synthetic data and increase sample density in sparse areas. This process preserves inter-modal relationships, mitigates data imbalance, improves domain adaptation, and boosts generalization. We also introduce a theoretical framework that quantifies the statistical accuracy improvements enabled by CoDSA as a function of synthetic sample volume and targeted region allocation, providing formal guarantees of its effectiveness. Extensive experiments demonstrate that CoDSA consistently outperforms non-adaptive augmentation strategies and state-of-the-art baselines in both supervised and unsupervised settings.

Problem

• Addresses limited and biased real-world datasets in machine learning
• Synthesizes high-fidelity data for underrepresented subpopulations using generative models
• Improves model performance and generalization across multimodal data domains

Innovation

• Generative models synthesize high-fidelity conditional data
• Transfer learning enhances synthetic data realism
• Theoretical framework quantifies statistical accuracy improvements