论文标题
通过基于分数的生成建模进行分类
Classification via score-based generative modelling
论文作者
论文摘要
在这项工作中，我们研究了基于得分的梯度学习在判别和生成分类设置中的应用。得分函数可用于将数据分布作为密度的替代方案。它可以通过分数匹配有效地学习，并用于灵活地生成可靠的样本以增强歧视性分类质量，以恢复密度并构建生成分类器。我们分析了涉及基于得分的表示的决策理论，并在模拟和现实世界数据集上进行了实验，证明了其在实现和改善二进制分类性能以及对扰动的鲁棒性方面的有效性，尤其是在高维度和不平衡情况下。
In this work, we investigated the application of score-based gradient learning in discriminative and generative classification settings. Score function can be used to characterize data distribution as an alternative to density. It can be efficiently learned via score matching, and used to flexibly generate credible samples to enhance discriminative classification quality, to recover density and to build generative classifiers. We analysed the decision theories involving score-based representations, and performed experiments on simulated and real-world datasets, demonstrating its effectiveness in achieving and improving binary classification performance, and robustness to perturbations, particularly in high dimensions and imbalanced situations.
