The concept of conditional computation for deep nets has been proposed
previously to improve model performance by selectively using only parts of the
model conditioned on the sample it is processing. In this paper, we investigate
input-dependent dynamic filter selection in deep convolutional neural networks
(CNNs). The problem is interesting because the idea of forcing different parts
of the model to learn from different types of samples may help us acquire
better filters in CNNs, improve the model generalization performance and
potentially increase the interpretability of model behavior. We propose a novel
yet simple framework called GaterNet, which involves a backbone and a gater
network. The backbone network is a regular CNN that performs the major
computation needed for making a prediction, while a global gater network is
introduced to generate binary gates for selectively activating filters in the
backbone network based on each input. Extensive experiments on CIFAR and
ImageNet datasets show that our models consistently outperform the original
models with a large margin. On CIFAR-10, our model also improves upon
state-of-the-art results.

本文提出了一种名为 GaterNet 的借助于门控网络实现的动态卷积神经网络滤波器选择框架，通过丰富 CNN 模型的学习方式，提高了模型性能并得出了比原论文更好的实验结果。