Misc,
ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions
(Sep 5, 2018)
Abstract
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.
