论文标题
在依赖类的域移位下的强大分类
Robust Classification under Class-Dependent Domain Shift
论文作者
论文摘要
对机器学习算法的调查鲁棒性对培训和测试分布之间的变化是一个积极的研究领域。在本文中，我们探讨了一种特殊类型的数据集偏移，我们称之为依赖类的域移动。它的特征是以下特征：输入数据因果关系取决于标签，数据中的变化是由已知变量完全解释的，控制移位的变量可以取决于标签，标签分布没有变化。我们通过信息理论约束定义一个简单的优化问题，并尝试通过神经网络解决该问题。玩具数据集上的实验表明，所提出的方法能够学习强大的分类器，这些分类器可以很好地推广到看不见的域。
Investigation of machine learning algorithms robust to changes between the training and test distributions is an active area of research. In this paper we explore a special type of dataset shift which we call class-dependent domain shift. It is characterized by the following features: the input data causally depends on the label, the shift in the data is fully explained by a known variable, the variable which controls the shift can depend on the label, there is no shift in the label distribution. We define a simple optimization problem with an information theoretic constraint and attempt to solve it with neural networks. Experiments on a toy dataset demonstrate the proposed method is able to learn robust classifiers which generalize well to unseen domains.
