most citedExploiting the Full Capacity of Deep Neural Networks while Avoiding Overfitting by Targeted Sparsity Regularization

3 citations · 3 across the 1 of their papers we have counts for

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cs.LG2020★ 3 cited

Exploiting the Full Capacity of Deep Neural Networks while Avoiding Overfitting by Targeted Sparsity Regularization

Karim Huesmann, Soeren Klemm, Lars Linsen +1

Overfitting is one of the most common problems when training deep neural networks on comparatively small datasets. Here, we demonstrate that neural network activation sparsity is a…