论文标题
一个多标签的持续学习框架，用于扩展包装设备监控的深度学习方法
A Multi-label Continual Learning Framework to Scale Deep Learning Approaches for Packaging Equipment Monitoring
论文作者
论文摘要
持续学习旨在从一系列任务中学习，同时记住新任务和旧任务。尽管提出了许多用于单级分类的方法，但在连续情况下，多标签分类仍然是一个具有挑战性的问题。我们第一次在域增量学习方案中研究多标签分类。此外，我们提出了一种有效的方法，该方法在任务数量方面具有对数复杂性，并且也可以在类增量学习方案中应用。我们在包装行业的现实世界多标签警报预测问题上验证了我们的方法。为了重现性，公开可用的数据集和用于实验的代码。
Continual Learning aims to learn from a stream of tasks, being able to remember at the same time both new and old tasks. While many approaches were proposed for single-class classification, multi-label classification in the continual scenario remains a challenging problem. For the first time, we study multi-label classification in the Domain Incremental Learning scenario. Moreover, we propose an efficient approach that has a logarithmic complexity with regard to the number of tasks, and can be applied also in the Class Incremental Learning scenario. We validate our approach on a real-world multi-label Alarm Forecasting problem from the packaging industry. For the sake of reproducibility, the dataset and the code used for the experiments are publicly available.
