  Recent progress in deep convolutional neural networks (CNNs) have enabled a
simple paradigm of architecture design: larger models typically achieve better
accuracy. Due to this, in modern CNN architectures, it becomes more important
to design models that generalize well under certain resource constraints, e.g.
the number of parameters. In this paper, we propose a simple way to improve the
capacity of any CNN model having large-scale features, without adding more
parameters. In particular, we modify a standard convolutional layer to have a
new functionality of channel-selectivity, so that the layer is trained to
select important channels to re-distribute their parameters. Our experimental
results under various CNN architectures and datasets demonstrate that the
proposed new convolutional layer allows new optima that generalize better via
efficient resource utilization, compared to the baseline.
