Bi-Discriminator Class-Conditional Tabular GAN
This work addresses data generation challenges for tabular data, which is incremental as it builds on existing GAN methods with specific architectural improvements.
The paper tackles the problem of synthesizing tabular datasets with mixed data types by introducing a bi-discriminator GAN with adapted preprocessing and a novel conditional term, achieving superior performance on four benchmark datasets in terms of likelihood fitness and machine learning efficacy.
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.