A Survey of Data Synthesis Approaches

2024/07/04 by Hsin‐Yu Chang, Pei‐Yu Chen, Chang, Hsin-Yu +11 · 4 citations
Computer Science · #Advanced Database Systems and Queries #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2407.03672

openalex publication_date 2024/07/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper provides a detailed survey of synthetic data techniques. We first discuss the expected goals of using synthetic data in data augmentation, which can be divided into four parts: 1) Improving Diversity, 2) Data Balancing, 3) Addressing Domain Shift, and 4) Resolving Edge Cases. Synthesizing data are closely related to the prevailing machine learning techniques at the time, therefore, we summarize the domain of synthetic data techniques into four categories: 1) Expert-knowledge, 2) Direct Training, 3) Pre-train then Fine-tune, and 4) Foundation Models without Fine-tuning. Next, we categorize the goals of synthetic data filtering into four types for discussion: 1) Basic Quality, 2) Label Consistency, and 3) Data Distribution. In section 5 of this paper, we also discuss the future directions of synthetic data and state three direction that we believe is important: 1) focus more on quality, 2) the evaluation of synthetic data, and 3) multi-model data augmentation.

Cited by

• SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework
• Towards High Supervised Learning Utility Training Data Generation: Data Pruning and Column Reordering
• Spatial Knowledge Graph-Guided Multimodal Synthesis
• SGD-Mix: Enhancing Domain-Specific Image Classification with Label-Preserving Data Augmentation

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