Convolutional neural networks (CNNs) have shown great capability of solving
various artificial intelligence tasks. However, the increasing model size has
raised challenges in employing them in resource-limited applications. In this
work, we propose to compress deep models by using channel-wise convolutions,
which re- place dense connections among feature maps with sparse ones in CNNs.
Based on this novel operation, we build light-weight CNNs known as ChannelNets.
Channel- Nets use three instances of channel-wise convolutions; namely group
channel-wise convolutions, depth-wise separable channel-wise convolutions, and
the convolu- tional classification layer. Compared to prior CNNs designed for
mobile devices, ChannelNets achieve a significant reduction in terms of the
number of parameters and computational cost without loss in accuracy. Notably,
our work represents the first attempt to compress the fully-connected
classification layer, which usually accounts for about 25% of total parameters
in compact CNNs. Experimental results on the ImageNet dataset demonstrate that
ChannelNets achieve consistently better performance compared to prior methods.

本文介绍了使用通道卷积压缩深度模型的方法，这种方法在 CNN 中替换特征图之间的稠密连接为稀疏连接，从而构建轻量级 CNN。ChannelNets 使用三种通道卷积的实例，并通过对 ImageNet 数据集进行实验来证明了其在参数和计算成本上的显著性降低，且不影响准确性。