Overcoming Long-term Catastrophic Forgetting through Adversarial Neural Pruning and Synaptic Consolidation
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Enabling a neural network to sequentially learn multiple tasks is of great significance for expanding the applicability of neural networks in realistic human application scenarios. However, as the task sequence increases, the model quickly forgets previously learned skills; we refer to this loss of memory of long sequences as long-term catastrophic forgetting. There are two main reasons for the long-term forgetting: first, as the tasks increase, the intersection of the low-error parameter subspace satisfying these tasks will become smaller and smaller or even non-existent; The second is the cumulative error in the process of protecting the knowledge of previous tasks. This paper, we propose a confrontation mechanism in which neural pruning and synaptic consolidation are used to overcome long-term catastrophic forgetting. This mechanism distills task-related knowledge into a small number of parameters, and retains the old knowledge by consolidating a small number of parameters, while sparing most parameters to learn the follow-up tasks, which not only avoids forgetting but also can learn a large number of tasks. Specifically, the neural pruning iteratively relaxes the parameter conditions of the current task to expand the common parameter subspace of tasks; The modified synaptic consolidation strategy is comprised of two components, a novel network structure information considered measurement is proposed to calculate the parameter importance, and a element-wise parameter updating strategy that is designed to prevent significant parameters being overridden in subsequent learning. We verified the method on image classification, and the results showed that our proposed ANPSC approach outperforms the state-of-the-art methods. The hyperparametric sensitivity test further demonstrates the robustness of our proposed approach.
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