Catastrophic forgetting of previous knowledge is a critical issue in continual learning typically handled through various regularization strategies. However, existing methods struggle especially when several incremental steps are performed. In this paper, we extend our previous approach (RECALL) and tackle forgetting by exploiting unsupervised web-crawled data to retrieve examples of old classes from online databases. Differently from the original approach that did not perform any evaluation of the web data, here we introduce two novel approaches based on adversarial learning and adaptive thresholding to select from web data only samples strongly resembling the statistics of the no longer available training ones. Furthermore, we improved the pseudo-labeling scheme to achieve a more accurate labeling of web data that also consider classes being learned in the current step. Experimental results show that this enhanced approach achieves remarkable results, especially when multiple incremental learning steps are performed.

翻译：先前知识的灾难性遗忘是持续学习中的关键问题，通常通过多种正则化策略来解决。然而，现有方法在执行多个增量步骤时仍面临挑战。本文在先前工作（RECALL）基础上进行扩展，利用无监督网络爬取数据从在线数据库中检索旧类样本以缓解遗忘。与原始方法未对网络数据进行评估不同，我们引入两种基于对抗学习和自适应阈值的新颖方法，仅从网络数据中筛选出与已不可获取的训练样本统计特征高度相似的样本。此外，我们改进了伪标签方案，使网络数据的标注更精确，同时兼顾当前步骤中正在学习的类别。实验结果表明，这种增强方法尤其在执行多步增量学习时取得了显著成效。