  Machine learning (ML) on tabular data is ubiquitous, yet obtaining abundant
high-quality tabular data for model training remains a significant obstacle.
Numerous works have focused on tabular data augmentation (TDA) to enhance the
original table with additional data, thereby improving downstream ML tasks.
Recently, there has been a growing interest in leveraging the capabilities of
generative AI for TDA. Therefore, we believe it is time to provide a
comprehensive review of the progress and future prospects of TDA, with a
particular emphasis on the trending generative AI. Specifically, we present an
architectural view of the TDA pipeline, comprising three main procedures:
pre-augmentation, augmentation, and post-augmentation. Pre-augmentation
encompasses preparation tasks that facilitate subsequent TDA, including error
handling, table annotation, table simplification, table representation, table
indexing, table navigation, schema matching, and entity matching. Augmentation
systematically analyzes current TDA methods, categorized into retrieval-based
methods, which retrieve external data, and generation-based methods, which
generate synthetic data. We further subdivide these methods based on the
granularity of the augmentation process at the row, column, cell, and table
levels. Post-augmentation focuses on the datasets, evaluation and optimization
aspects of TDA. We also summarize current trends and future directions for TDA,
highlighting promising opportunities in the era of generative AI. In addition,
the accompanying papers and related resources are continuously updated and
maintained in the GitHub repository at
https://github.com/SuDIS-ZJU/awesome-tabular-data-augmentation to reflect
ongoing advancements in the field.
