论文标题
持续的功能选择：持续学习的虚假特征
Continual Feature Selection: Spurious Features in Continual Learning
论文作者
论文摘要
持续学习（CL）是研究领域，无需忘记数据分布何时不是静态的研究。本文研究了虚假特征对持续学习算法的影响。我们表明，持续学习算法通过选择不可推广的功能来解决任务。我们的实验强调，持续学习算法遇到了两个相关问题：（1）虚假特征和（2）本地伪造特征。第一个是由于训练和测试数据之间的协变量造成的，而第二个是由于每个培训步骤中对数据的访问有限。我们通过一系列持续的学习实验来研究（1），以改变虚假相关量和数据分布支持。我们表明（2）是持续学习以及灾难性遗忘的主要原因。本文通过强调（本地）虚假特征在算法功能中的影响，提出了一种不同的理解性能降低方式。
Continual Learning (CL) is the research field addressing learning without forgetting when the data distribution is not static. This paper studies spurious features' influence on continual learning algorithms. We show that continual learning algorithms solve tasks by selecting features that are not generalizable. Our experiments highlight that continual learning algorithms face two related problems: (1) spurious features and (2) local spurious features. The first one is due to a covariate shift between training and testing data, while the second is due to the limited access to data at each training step. We study (1) through a consistent set of continual learning experiments varying spurious correlation amount and data distribution support. We show that (2) is a major cause of performance decrease in continual learning along with catastrophic forgetting. This paper presents a different way of understanding performance decrease in continual learning by highlighting the influence of (local) spurious features in algorithms capabilities.
