Abstract
Catastrophic forgetting, which means that old tasks are forgotten mostly when new tasks are learned, is a crucial problem of neural networks for autonomous robots. This problem is due to backpropagation overwrites all network parameters, and therefore, can be solved by not overwriting important parameters for the old tasks. Hence, regularization methods, represented by elastic weight consolidation, give the globally stable equilibrium points to the optimal parameters for the old tasks. They unfortunately aim to hold all parameters, even if the regularization is weak. This paper therefore proposes a regularization method, named Check regularization, to consolidate only the important parameters for the tasks and to initialize the other parameters preparing for the future tasks. Simulations with two tasks to be learned sequentially show that the proposed method outperforms the previous method under a condition where the interference between the tasks is severe.
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This research has been supported by the Kayamori Foundation of Information Science Advancement.
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Kobayashi, T. (2018). Check Regularization: Combining Modularity and Elasticity for Memory Consolidation. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11140. Springer, Cham. https://doi.org/10.1007/978-3-030-01421-6_31
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