  Modern deep neural networks require a significant amount of computing time
and power to train and deploy, which limits their usage on edge devices.
Inspired by the iterative weight pruning in the Lottery Ticket Hypothesis, we
propose DropNet, an iterative pruning method which prunes nodes/filters to
reduce network complexity. DropNet iteratively removes nodes/filters with the
lowest average post-activation value across all training samples. Empirically,
we show that DropNet is robust across diverse scenarios, including MLPs and
CNNs using the MNIST, CIFAR-10 and Tiny ImageNet datasets. We show that up to
90% of the nodes/filters can be removed without any significant loss of
accuracy. The final pruned network performs well even with reinitialization of
the weights and biases. DropNet also has similar accuracy to an oracle which
greedily removes nodes/filters one at a time to minimise training loss,
highlighting its effectiveness.
