论文标题
多标签分类中的课堂内终身学习
Class-Incremental Lifelong Learning in Multi-Label Classification
论文作者
论文摘要
现有的类新生终身学习研究仅使用单标签，这限制了其对多标签数据的适应性。本文研究了终生多标签（LML）分类，该分类在连续的多标签分类数据流中构建了在线类式分类器。在LML分类中使用部分标签的数据培训可能会导致旧课程更严重的灾难性遗忘。为了解决该问题，研究提出了一个增强图卷积网络（AGCN），并在顺序的部分标签任务中具有建筑增强相关矩阵（ACM）。两个基准的结果表明，该方法可有效地分类和减少遗忘。
Existing class-incremental lifelong learning studies only the data is with single-label, which limits its adaptation to multi-label data. This paper studies Lifelong Multi-Label (LML) classification, which builds an online class-incremental classifier in a sequential multi-label classification data stream. Training on the data with Partial Labels in LML classification may result in more serious Catastrophic Forgetting in old classes. To solve the problem, the study proposes an Augmented Graph Convolutional Network (AGCN) with a built Augmented Correlation Matrix (ACM) across sequential partial-label tasks. The results of two benchmarks show that the method is effective for LML classification and reducing forgetting.
