论文标题
部分可观测时空混沌系统的无模型预测
Tabular GANs for uneven distribution
论文作者
论文摘要
甘斯以现实的图像生成成功而闻名。但是，它们也可以应用于表格数据生成。我们将审查并检查一些有关行动中表格剂的最近论文。我们将生成数据以使火车分配更接近测试。然后将在初始火车数据集中训练的模型性能与在火车上使用GAN生成的数据进行训练的模型性能，还通过对抗训练对火车进行训练。我们表明，如果火车和测试数据之间的数据分布不平衡，则使用GAN可能是一种选择。
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 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.
