Score-based generative models (SGMs) are a recent breakthrough in generating fake images. SGMs are known to surpass other generative models, e.g., generative adversarial networks (GANs) and variational autoencoders (VAEs). Being inspired by their big success, in this work, we fully customize them for generating fake tabular data. In particular, we are interested in oversampling minor classes since imbalanced classes frequently lead to sub-optimal training outcomes. To our knowledge, we are the first presenting a score-based tabular data oversampling method. Firstly, we re-design our own score network since we have to process tabular data. Secondly, we propose two options for our generation method: the former is equivalent to a style transfer for tabular data and the latter uses the standard generative policy of SGMs. Lastly, we define a fine-tuning method, which further enhances the oversampling quality. In our experiments with 6 datasets and 10 baselines, our method outperforms other oversampling methods in all cases.

本论文在以往 score-based generative models 成功生成虚假图像的基础上，自主研发了在 tabular data 数据集上使用的 score-based 过采样方法，该方法采用自有的 score network，提出了两种生成方法，进一步使用 fine-tuning 方法改进，实验证明在 6 个数据集上，该方法均能优于其他过采样方法。

SOS：针对表格数据的基于得分的过采样