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Implicit Filter Sparsification In Convolutional Neural Networks
[article]
2019
arXiv
pre-print
We show implicit filter level sparsity manifests in convolutional neural networks (CNNs) which employ Batch Normalization and ReLU activation, and are trained with adaptive gradient descent techniques and L2 regularization or weight decay. Through an extensive empirical study (Mehta et al., 2019) we hypothesize the mechanism behind the sparsification process, and find surprising links to certain filter sparsification heuristics proposed in literature. Emergence of, and the subsequent pruning of
arXiv:1905.04967v1
fatcat:2zqa53evrrgiblqbqxx24fvaea