  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.
