A Multi-label Continual Learning Framework to Scale Deep Learning Approaches for Packaging Equipment Monitoring

• URL: http://arxiv.org/abs/2208.04227v1
• Date: Mon, 8 Aug 2022 15:58:39 GMT
• Title: A Multi-label Continual Learning Framework to Scale Deep Learning Approaches for Packaging Equipment Monitoring
• Authors: Davide Dalle Pezze, Denis Deronjic, Chiara Masiero, Diego Tosato, Alessandro Beghi, Gian Antonio Susto
• Abstract summary: We study multi-label classification in the continual scenario for the first time. We propose an efficient approach that has a logarithmic complexity with regard to the number of tasks. We validate our approach on a real-world multi-label Forecasting problem from the packaging industry.
• Score: 57.5099555438223
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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