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While the concept of a Sparse Neural Network has been researched for sometime, researchers have only recently made notable progress in the matter.Techniques like Sparse Evolutionary Training allow for significantly lowercomputational complexity when compared to fully connected models by reducingredundant connections. That typically takes place in an iterative process ofweight creation and removal during network training. Although there have beennumerous approaches to optimize the redistribution of the removed weights,there seems to be little or no study on the effect of activation functions onthe performance of the Sparse Networks. This research provides insights intothe relationship between the activation function used and the networkperformance at various sparsity levels.

Activation function impact on Sparse Neural Networks