Catastrophic forgetting (CF) occurs when a neural network loses the information previously learned while training on a set of samples from a different distribution, i.e., a new task. Existing approaches have achieved remarkable results in mitigating CF, especially in a scenario called task incremental learning. However, this scenario is not realistic, and limited work has been done to achieve good results on more realistic scenarios. In this paper, we propose a novel regularization method called Centroids Matching, that, inspired by meta-learning approaches, fights CF by operating in the feature space produced by the neural network, achieving good results while requiring a small memory footprint. Specifically, the approach classifies the samples directly using the feature vectors produced by the neural network, by matching those vectors with the centroids representing the classes from the current task, or all the tasks up to that point. Centroids Matching is faster than competing baselines, and it can be exploited to efficiently mitigate CF, by preserving the distances between the embedding space produced by the model when past tasks were over, and the one currently produced, leading to a method that achieves high accuracy on all the tasks, without using an external memory when operating on easy scenarios, or using a small one for more realistic ones. Extensive experiments demonstrate that Centroids Matching achieves accuracy gains on multiple datasets and scenarios.

翻译：当神经网络丢失了以前学到的信息,而关于不同分布的一组样本的培训(即新任务)却发生了灾难性的遗忘(CF),当神经网络丧失了以前学到的信息时,就会发生灾难性的遗忘(CF),而关于不同分布的一组样本的培训(即新任务),现有方法在减缓CFF方面已经取得了显著的成果,特别是在称为任务递增学习的情景中。然而,这一假设并不现实,而且为了在更现实的情景中取得良好结果而开展的工作也很有限。在本文中,我们提出了一个叫做Centraids Matching(Centraids Matching)的新颖的正规化方法,在元学习方法的启发下,通过在神经网络生成的地貌空间中运作,实现良好的结果,同时需要小的记忆足迹。具体地,该方法将样品直接分类为使用神经网络生成的特性矢量,特别是用代表当前任务或所有任务到该点之前的所有任务中的所有任务中的分解体匹配。 中心比相基准要快,并且可以用来有效减轻CFFE的距离,在以往任务和目前制作的一种方法,从而实现一种更精确的精准性,在不甚远的外部任务上不使用较易的模型上展示。