Summary of Taegan: Generating Synthetic Tabular Data For Data Augmentation, by Jiayu Li et al.
TAEGAN: Generating Synthetic Tabular Data For Data Augmentation
by Jiayu Li, Zilong Zhao, Kevin Yee, Uzair Javaid, Biplab Sikdar
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes Tabular Auto-Encoder Generative Adversarial Network (TAEGAN), a novel framework for generating high-quality tabular data. Unlike existing methods, TAEGAN focuses on small datasets and addresses the scarcity issue in synthetic tabular data generation. The proposed method employs a masked auto-encoder as the generator, leveraging self-supervised pre-training to improve its performance. Experimental results show that TAEGAN outperforms state-of-the-art deep-learning-based tabular data generation models on 9 out of 10 datasets in terms of machine learning efficacy and achieves superior data augmentation performance on 7 out of 8 smaller datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to create fake table data that’s realistic and useful for testing software or sharing private information. Most research has focused on making large datasets, but this paper looks at how to make fake data for smaller datasets too. The method uses a special type of artificial intelligence called a masked auto-encoder to generate the fake data. The results show that this new method works better than other deep-learning-based methods in most cases. |
Keywords
» Artificial intelligence » Data augmentation » Deep learning » Encoder » Generative adversarial network » Machine learning » Self supervised
