Tabular GANs for uneven distribution
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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 recent papers about tabular GANs inaction. We will generate data to make train distribution bring closer to the test. Then compare model performance trained on the initial train dataset, 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 be-tween train and test data.
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