Tabular GANs for uneven distribution

Abstract: GANs are well known for success in the realistic image generation. However, they can be applied in tabular data generation as well. We will review and examine some papers about tabular GANs in action. We will generate data to make train distribution bring closer to the test. Then compare model performance trained on the initial train dataset, with trained on the train with GAN generated data, also we train the model by sampling train by adversarial training. We show that using GAN might be an option in case of uneven data distribution between train and test data.