论文标题
XSEPCONV：极度分开的卷积
XSepConv: Extremely Separated Convolution
论文作者
论文摘要
深度卷积逐渐成为现代有效神经网络的必不可少的操作，并且最近已应用于它的较大核大小（$ \ ge5 $）。在本文中，我们提出了一种新型的极度分离的卷积块（XSEPCONV），该卷积将空间分开的卷积融合到深度卷积中，以进一步降低大核的计算成本和参数大小。此外，采用了额外的$ 2 \ times2 $深度卷积以及改进的对称填充策略来补偿可空间可分离的卷积带来的副作用。 XSEPCONV设计为具有较大核尺寸的香草深度卷积的有效替代品。为了验证这一点，我们将XSEPCONV用于最先进的体系结构MobilenetV3-Small，并对四个竞争激烈的基准数据集（CIFAR-10，CIFAR-10，CIFAR-100，SVHN和TINY-IMAGENET）进行了广泛的实验，以证明XsepConv确实可以在精确率和优先级之间进行更好的折衷。
Depthwise convolution has gradually become an indispensable operation for modern efficient neural networks and larger kernel sizes ($\ge5$) have been applied to it recently. In this paper, we propose a novel extremely separated convolutional block (XSepConv), which fuses spatially separable convolutions into depthwise convolution to further reduce both the computational cost and parameter size of large kernels. Furthermore, an extra $2\times2$ depthwise convolution coupled with improved symmetric padding strategy is employed to compensate for the side effect brought by spatially separable convolutions. XSepConv is designed to be an efficient alternative to vanilla depthwise convolution with large kernel sizes. To verify this, we use XSepConv for the state-of-the-art architecture MobileNetV3-Small and carry out extensive experiments on four highly competitive benchmark datasets (CIFAR-10, CIFAR-100, SVHN and Tiny-ImageNet) to demonstrate that XSepConv can indeed strike a better trade-off between accuracy and efficiency.
