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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2407.13016 |
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| _version_ | 1866911959340810240 |
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| author | Shulakov, Volodymyr |
| author_facet | Shulakov, Volodymyr |
| contents | Synthetic tabular data is becoming a necessity as concerns about data privacy intensify in the world. Tabular data can be useful for testing various systems, simulating real data, analyzing the data itself or building predictive models. Unfortunately, such data may not be available due to confidentiality issues. Previous techniques, such as TVAE (Xu et al., 2019) or OCTGAN (Kim et al., 2021), are either unable to handle particularly complex datasets, or are complex in themselves, resulting in inferior run time performance. This paper introduces PSVAE, a new simple model that is capable of producing high-quality synthetic data in less run time. PSVAE incorporates two key ideas: loss optimization and post-selection. Along with these ideas, the proposed model compensates for underrepresented categories and uses a modern activation function, Mish (Misra, 2019). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_13016 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | High-Quality Tabular Data Generation using Post-Selected VAE Shulakov, Volodymyr Machine Learning Software Engineering G.3 Synthetic tabular data is becoming a necessity as concerns about data privacy intensify in the world. Tabular data can be useful for testing various systems, simulating real data, analyzing the data itself or building predictive models. Unfortunately, such data may not be available due to confidentiality issues. Previous techniques, such as TVAE (Xu et al., 2019) or OCTGAN (Kim et al., 2021), are either unable to handle particularly complex datasets, or are complex in themselves, resulting in inferior run time performance. This paper introduces PSVAE, a new simple model that is capable of producing high-quality synthetic data in less run time. PSVAE incorporates two key ideas: loss optimization and post-selection. Along with these ideas, the proposed model compensates for underrepresented categories and uses a modern activation function, Mish (Misra, 2019). |
| title | High-Quality Tabular Data Generation using Post-Selected VAE |
| topic | Machine Learning Software Engineering G.3 |
| url | https://arxiv.org/abs/2407.13016 |