Designing light-weight CNN models with little parameters and Flops is a
prominent research concern. However, three significant issues persist in the
current light-weight CNNs: i) the lack of architectural consistency leads to
redundancy and hindered capacity comparison, as well as the ambiguity in
causation between architectural choices and performance enhancement; ii) the
utilization of a single-branch depth-wise convolution compromises the model
representational capacity; iii) the depth-wise convolutions account for large
proportions of parameters and Flops, while lacking efficient method to make
them light-weight. To address these issues, we factorize the four vital
components of light-weight CNNs from coarse to fine and redesign them: i) we
design a light-weight overall architecture termed LightNet, which obtains
better performance by simply implementing the basic blocks of other
light-weight CNNs; ii) we abstract a Meta Light Block, which consists of
spatial operator and channel operator and uniformly describes current basic
blocks; iii) we raise RepSO which constructs multiple spatial operator branches
to enhance the representational ability; iv) we raise the concept of receptive
range, guided by which we raise RefCO to sparsely factorize the channel
operator. Based on above four vital components, we raise a novel light-weight
CNN model termed as FalconNet. Experimental results validate that FalconNet can
achieve higher accuracy with lower number of parameters and Flops compared to
existing light-weight CNNs.

通过设计 LightNet 总体架构、抽象出 Meta Light Block、提高表示能力的空间运算器和提高初始块的通道运算器以及引入感受野的概念，我们提出了一个名为 FalconNet 的新颖轻量级卷积神经网络模型，实验结果表明，与现有的轻量级 CNN 相比，FalconNet 可以用较少的参数和 Flops 实现更高的准确性。

FalconNet: 轻量级卷积神经网络的分解

FalconNet: Factorization for the Light-weight ConvNets

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。