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When learning tasks over time, artificial neural networks suffer from aproblem known as Catastrophic Forgetting (CF). This happens when the weights ofa network are overwritten during the training of a new task causing forgettingof old information. To address this issue, we propose MetA Reusable Knowledgeor MARK, a new method that fosters weight reusability instead of overwritingwhen learning a new task. Specifically, MARK keeps a set of shared weightsamong tasks. We envision these shared weights as a common Knowledge Base (KB)that is not only used to learn new tasks, but also enriched with new knowledgeas the model learns new tasks. Key components behind MARK are two-fold. On theone hand, a metalearning approach provides the key mechanism to incrementallyenrich the KB with new knowledge and to foster weight reusability among tasks.On the other hand, a set of trainable masks provides the key mechanism toselectively choose from the KB relevant weights to solve each task. By usingMARK, we achieve state of the art results in several popular benchmarks,surpassing the best performing methods in terms of average accuracy by over 10%on the 20-Split-MiniImageNet dataset, while achieving almost zero forgetfulnessusing 55% of the number of parameters. Furthermore, an ablation study providesevidence that, indeed, MARK is learning reusable knowledge that is selectivelyused by each task.

Optimizing Reusable Knowledge for Continual Learning via Metalearning