We introduce a new continual (or lifelong) learning algorithm called LDA-CP&S that performs segmentation tasks without undergoing catastrophic forgetting. The method is applied to two different surface defect segmentation problems that are learned incrementally, i.e. providing data about one type of defect at a time, while still being capable of predicting every defect that was seen previously. Our method creates a defect-related subnetwork for each defect type via iterative pruning and trains a classifier based on linear discriminant analysis (LDA). At the inference stage, we first predict the defect type with LDA and then predict the surface defects using the selected subnetwork. We compare our method with other continual learning methods showing a significant improvement -- mean Intersection over Union better by a factor of two when compared to existing methods on both datasets. Importantly, our approach shows comparable results with joint training when all the training data (all defects) are seen simultaneously

翻译：我们提出了一种名为LDA-CP&S的新型持续（或终身）学习算法，该算法在执行分割任务时不会发生灾难性遗忘。该方法应用于两个不同的表面缺陷分割问题，这些问题以增量方式学习，即每次仅提供一种缺陷类型的数据，同时仍能预测先前见过的所有缺陷。我们的方法通过迭代剪枝为每种缺陷类型创建缺陷相关的子网络，并基于线性判别分析（LDA）训练分类器。在推理阶段，我们首先利用LDA预测缺陷类型，然后使用所选子网络预测表面缺陷。我们将该方法与其他持续学习方法进行比较，结果显示在两种数据集上，其平均交并比相较于现有方法均提升了约两倍。重要的是，当所有训练数据（所有缺陷）同时呈现时，我们的方法达到了与联合训练相当的结果。