基于权值筛选策略的增量学习方法

Incremental learning based on weight selection strategy

为提高现有增量学习模型在容量固定环境下学习的持久性,提出一种基于权值选择策略的增量学习方法。根据贝叶斯神经网络携带的不确定性动态地调整权值的学习率,以此优化一个能同时记忆新旧知识的模型;为使模型的学习与记忆更有弹性,在此基础上提出一种权值选择策略,该策略可以令模型主动选择性地释放部分网络资源,在不严重损害旧任务性能的前提下促进后续任务的学习。实验结果表明,在模型容量固定的环境下,权值选择策略的引入可以更有效地发掘模型的持续学习能力。

To improve the learning persistence of the existing incremental learning models in the capacity fixed environment,a Bayesian incremental learning method based on weight selection strategy was proposed.The learning rate was dynamically adjusted according to the uncertainty carried by Bayesian neural networks,and a model that could remember the old and new know-ledge simultaneously was optimized.To explore the continuous learning ability of the model more fully,a weight selection strategy was proposed,which guided the model to selectively release some weights to promote the learning of future tasks without serious-ly damaging the performance of the old tasks.Experimental results show that the resource releasing mechanism can more effectively explore the continuous learning ability of the model under the fixed capacity environment.