针对深度学习系统在增量式场景下进行图像分类时产生的灾难性遗忘问题，提出了一种基于神经元正则和资源释放的增量学习方法.该方法以贝叶斯神经网络为基础框架，首先以神经元为单位对输入权值进行分组并按组将权值的标准差限制为相同的值，然后在训练过程中根据统一后的标准差对每组权值的调整执行相应强度的正则.最后，为了提高模型的持续学习能力，提出一个资源释放机制.该机制通过引导模型选择性稀释部分权值的正则强度来保持模型的学习能力.在几个公开数据集上的实验结果表明，本研究提出的方法可以更有效地发掘模型的持续学习能力，即使在容量有限的环境下，也能学习到一个性能更好的模型.

Aimimg at the catastrophic forgetting problem caused by the image classification of deep learning systems in an incremental scene. A incremental learning method which based on neuron regularization and resource releasing mechanism was proposed. This method is based on the framework of Bayesian neural network. Firstly, grouping the input weights by neurons and restrict the standard deviation of weights to the same value according to the groups. Then in the training process, the corresponding strength regularization was performed for the weights of each group according to the standard deviation after unification. Finally, in order to improve model's continuous learning ability, a resource releasing mechanism was proposed. This mechanism maintains model's learning ability by guiding the model to selectively dilute the regularization strengths of some weights. Experiments on several common datasets show that the proposed method can explore the continuous learning capability of the model more effectively, and a better model can be learned even in a fixed capacity environment.