Deep convolutional networks are well-known for their high computational and
memory demands. Given limited resources, how does one design a network that
balances its size, training time, and prediction accuracy? A surprisingly
effective approach to trade accuracy for size and speed is to simply reduce the
number of channels in each convolutional layer by a fixed fraction and retrain
the network. In many cases this leads to significantly smaller networks with
only minimal changes to accuracy. In this paper, we take a step further by
empirically examining a strategy for deactivating connections between filters
in convolutional layers in a way that allows us to harvest savings both in
run-time and memory for many network architectures. More specifically, we
generalize 2D convolution to use a channel-wise sparse connection structure and
show that this leads to significantly better results than the baseline approach
for large networks including VGG and Inception V3.

通过实验证明了通过在卷积层之间停用连接的方法将采用通道稀疏连接结构的 2D 卷积技术与许多网络体系结构一起使用，可以在保持准确性的同时实现明显的运行时间和内存储存器的节省。