  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.
