Nearly-tight VC-dimension bounds for piecewise linear neural networks
Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1064-1068, 2017.
We prove new upper and lower bounds on the VC-dimension of deep neural networks with the ReLU activation function. These bounds are tight for almost the entire range of parameters. Letting $W$ be the number of weights and $L$ be the number of layers, we prove that the VC-dimension is $O(W L \log(W))$, and provide examples with VC-dimension $Ω( W L \log(W/L) )$. This improves both the previously known upper bounds and lower bounds. In terms of the number $U$ of non-linear units, we prove a tight bound $Θ(W U)$ on the VC-dimension. All of these results generalize to arbitrary piecewise linear activation functions.