Empirical investigations on WVA structural issues

In this paper we want to present the results of empirical verification of some issues concerning the methods for overcoming catastrophic forgetting in neural networks. First, in the introduction, we will try to describe in detail the problem of catastrophic forgetting and methods for overcoming it for those who are not yet familiar with this topic. Then we will discuss the essence and limitations of the WVA method which we presented in previous papers. Further, we will touch upon the issues of applying the WVA method to gradients or optimization steps of weights, choosing the optimal attenuation function in this method, as well as choosing the optimal hyper-parameters of the method depending on the number of tasks in sequential training of neural networks.

Stabilizing Elastic Weight Consolidation method in practical ML tasks and using weight importances for neural network pruning

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.