A Multi-label Continual Learning Framework to Scale Deep Learning Approaches for Packaging Equipment Monitoring
This work addresses a challenging problem in industrial monitoring for the packaging industry, though it appears incremental as it extends existing continual learning methods to multi-label scenarios.
The paper tackles multi-label classification in continual learning, specifically introducing a framework for Domain Incremental Learning and applying it to a real-world alarm forecasting problem in packaging equipment monitoring, achieving logarithmic complexity with respect to tasks.
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.