标题： 持续学习者的新型度量标准 显示英文标题

标题： New metrics for analyzing continual learners

摘要： 深度神经网络在从固定类别集合中独立同分布的数据上进行训练时表现出色。 然而，在现实场景中，可能希望在连续的数据流上训练模型，其中多个分类任务依次出现。 这种场景称为持续学习（CL），对标准学习算法提出了挑战，这些算法在学习新任务的同时难以保持对旧任务的知识。 这种稳定性和可塑性的权衡仍是CL的核心问题，已经提出了多种度量来分别准确测量稳定性和可塑性。 然而，没有一种度量考虑了分类任务难度的增加，这会导致任何模型的性能下降。 从这个角度看，我们分析了当前度量的一些局限性，并确定了设置引起的遗忘的存在。 因此，我们提出了考虑任务难度增加的新度量。 通过在基准数据集上的实验，我们证明了所提出的度量可以为持续学习环境中模型实现的稳定性-可塑性权衡提供新的见解。 显示英文摘要

摘要： Deep neural networks have shown remarkable performance when trained on independent and identically distributed data from a fixed set of classes. However, in real-world scenarios, it can be desirable to train models on a continuous stream of data where multiple classification tasks are presented sequentially. This scenario, known as Continual Learning (CL) poses challenges to standard learning algorithms which struggle to maintain knowledge of old tasks while learning new ones. This stability-plasticity dilemma remains central to CL and multiple metrics have been proposed to adequately measure stability and plasticity separately. However, none considers the increasing difficulty of the classification task, which inherently results in performance loss for any model. In that sense, we analyze some limitations of current metrics and identify the presence of setup-induced forgetting. Therefore, we propose new metrics that account for the task's increasing difficulty. Through experiments on benchmark datasets, we demonstrate that our proposed metrics can provide new insights into the stability-plasticity trade-off achieved by models in the continual learning environment.