mlsanityData to 7 Oct 2026

Bi-Discriminator Class-Conditional Tabular GAN

arXiv 2111.06549 ↗First posted 12 Nov 2021cs.LGcited by 3

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Abstract (arXiv)

This paper introduces a bi-discriminator GAN for synthesizing tabular datasets containing continuous, binary, and discrete columns. Our proposed approach employs an adapted preprocessing scheme and a novel conditional term for the generator network to more effectively capture the input sample distributions. Additionally, we implement straightforward yet effective architectures for discriminator networks aiming at providing more discriminative gradient information to the generator. Our experimental results on four benchmarking public datasets corroborates the superior performance of our GAN both in terms of likelihood fitness metric and machine learning efficacy.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 35 of its 79 references that are in the map. First posted on arXiv 12 Nov 2021.

Builds on

35 of 79 references.

References · 35

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23 more
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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 1.

3 papers.

  1. 2024P-TA: Using Proximal Policy Optimization to Enhance Tabular Data Augmentation via Large Language Modelscited by 4
  2. 2023CasTGAN: Cascaded Generative Adversarial Network for Realistic Tabular Data Synthesiscited by 3
  3. 2022RCC-GAN: Regularized Compound Conditional GAN for Large-Scale Tabular Data Synthesiscited by 0