One major obstacle towards AI is the poor ability of models to solve new problems quicker, and without forgetting previously acquired knowledge. To better understand this issue, we study the problem of continual learning, where the model observes, once and one by one, examples concerning a sequence of tasks. First, we propose a set of metrics to evaluate models learning over a continuum of data. These metrics characterize models not only by their test accuracy, but also in terms of their ability to transfer knowledge across tasks. Second, we propose a model for continual learning, called Gradient Episodic Memory (GEM) that alleviates forgetting, while allowing beneficial transfer of knowledge to previous tasks. Our experiments on variants of the MNIST and CIFAR-100 datasets demonstrate the strong performance of GEM when compared to the state-of-the-art.

翻译：AI的一个主要障碍是模型能力差,无法更快地解决新的问题,而且不能忘记以前获得的知识。为了更好地了解这一问题,我们研究持续学习的问题,模型一一一观察一系列任务的例子。首先,我们提出一套衡量标准,评价模型在连续数据方面学习的情况。这些衡量标准不仅根据测试的准确性来描述模型,而且从其跨任务转让知识的能力来描述模型。第二,我们提出一个持续学习的模式,称为“进步记忆”,以缓解遗忘,同时允许将知识有益地转让给先前的任务。我们对MNIST和CIFAR-100数据集变量的实验表明GEM在与最新数据相比的强劲表现。