One of the objectives of Continual Learning is to learn new concepts continually over a stream of experiences and at the same time avoid catastrophic forgetting. To mitigate complete knowledge overwriting, memory-based methods store a percentage of previous data distributions to be used during training. Although these methods produce good results, few studies have tested their out-of-distribution generalization properties, as well as whether these methods overfit the replay memory. In this work, we show that although these methods can help in traditional in-distribution generalization, they can strongly impair out-of-distribution generalization by learning spurious features and correlations. Using a controlled environment, the Synbol benchmark generator (Lacoste et al., 2020), we demonstrate that this lack of out-of-distribution generalization mainly occurs in the linear classifier.

翻译：持续学习的目标之一是在连续的经验流中不断学习新概念，同时避免灾难性遗忘。为缓解完全的知识覆盖，基于记忆的方法会存储一定比例的先前数据分布，以供训练时使用。尽管这些方法取得了良好效果，但很少有研究检验它们的分布外泛化特性，以及这些方法是否会对回放记忆产生过拟合。在本工作中，我们表明：尽管此类方法有助于传统的分布内泛化，但它们可能通过学习虚假特征和相关性，严重损害分布外泛化能力。通过使用受控环境——Synbol基准生成器（Lacoste等，2020），我们证明这种分布外泛化能力的缺失主要发生在线性分类器中。