利用单通道内卷积、拓扑细分和空间“瓶颈”结构设计高效卷积层
Design of Efficient Convolutional Layers using Single Intra-channel Convolution, Topological Subdivisioning and Spatial "Bottleneck" Structure
摘要：
深度卷积神经网络以高计算复杂性为代价实现了卓越的视觉识别性能。在本文中，我们基于三种方案设计了一种新的高效卷积层。卷积层中的3D卷积操作可以被认为是同时在每个通道中执行空间卷积和跨通道的线性投影。通过解开它们并顺序排列空间卷积，所提出的层由单个通道内卷积和线性通道投影组成，其中计算可以忽略不计。采用拓扑细分的方法减少了输入输出通道之间的连接。此外，我们还引入了一个空间“瓶颈”结构，利用卷积-投影-反卷积流水线来利用输入中相邻像素之间的相关性。我们的实验表明，所提出的层在准确度/复杂度比方面明显优于标准卷积层。我们的模型实现了与VGG，ResNet-50，ResNet-101相似的精度，同时分别需要42，4.5，6.5倍的计算量。
英文摘要：
Deep convolutional neural networks achieve remarkable visual recognition performance, at the cost of high computational complexity. In this paper, we have a new design of efficient convolutional layers based on three schemes. The 3D convolution operation in a convolutional layer can be considered as performing spatial convolution in each channel and linear projection across channels simultaneously. By unravelling them and arranging the spatial convolution sequentially, the proposed layer is composed of a single intra-channel convolution, of which the computation is negligible, and a linear channel projection. A topological subdivisioning is adopted to reduce the connection between the input channels and output channels. Additionally, we also introduce a spatial "bottleneck" structure that utilizes a convolution-projection-deconvolution pipeline to take advantage of the correlation between adjacent pixels in the input. Our experiments demonstrate that the proposed layers remarkably outperform the standard convolutional layers with regard to accuracy/complexity ratio. Our models achieve similar accuracy to VGG, ResNet-50, ResNet-101 while requiring 42, 4.5, 6.5 times less computation respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
