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