This paper is devoted to the features of the practical application of Elastic Weight Consolidation method. Here we will more rigorously compare the known methodologies for calculating the importance of weights when applied to networks with fully connected and convolutional layers. We will also point out the problems that arise when applying the Elastic Weight Consolidation method in multilayer neural networks with convolutional layers and self-attention layers, and propose method to overcome these problems. In addition, we will notice an interesting fact about the use of various types of weight importance in the neural network pruning task.

翻译：本文专门论述弹性重量集成法实际应用的特点。在这里,我们将更严格地比较已知的计算加权重要性的方法,这些方法将适用于完全连通和富集层的网络。我们还将指出在将弹性重量集成法应用于具有富集层和自我注意层的多层神经网络时出现的问题,并提出解决这些问题的方法。此外,我们将注意到在神经网络运行任务中使用各种重量重要性的有趣事实。