Feature reuse has been a key technique in light-weight convolutional neural networks (CNNs) architecture design. Current methods usually utilize a concatenation operator to keep large channel numbers cheaply (thus large network capacity) by reusing feature maps from other layers. Although concatenation is parameters- and FLOPs-free, its computational cost on hardware devices is non-negligible. To address this, this paper provides a new perspective to realize feature reuse implicitly and more efficiently instead of concatenation. A novel hardware-efficient RepGhost module is proposed for implicit feature reuse via reparameterization, instead of using concatenation operator. Based on the RepGhost module, we develop our efficient RepGhost bottleneck and RepGhostNet. Experiments on ImageNet and COCO benchmarks demonstrate that our RepGhostNet is much more effective and efficient than GhostNet and MobileNetV3 on mobile devices. Specially, our RepGhostNet surpasses GhostNet 0.5x by 2.5% Top-1 accuracy on ImageNet dataset with less parameters and comparable latency on an ARM-based mobile device. Code and model weights are available at https://github.com/ChengpengChen/RepGhost.

翻译：特征重用一直是轻量级卷积神经网络（CNN）架构设计中的关键技术。当前方法通常利用拼接操作，通过复用其他层的特征图来廉价地保持较大的通道数（从而获得较大的网络容量）。尽管拼接操作不增加参数和浮点运算量，但其在硬件设备上的计算开销不可忽视。为解决此问题，本文提供了一种新视角，以隐式且更高效的方式实现特征重用，而非采用拼接操作。我们提出了一种新颖的硬件高效RepGhost模块，通过重参数化实现隐式特征重用，而非使用拼接操作符。基于RepGhost模块，我们开发了高效的RepGhost瓶颈结构和RepGhostNet。在ImageNet和COCO基准测试上的实验表明，我们的RepGhostNet在移动设备上比GhostNet和MobileNetV3更为高效。特别地，在ImageNet数据集上，我们的RepGhostNet以更少的参数和在基于ARM的移动设备上相当的延迟，超越了GhostNet 0.5x版本2.5%的Top-1准确率。代码和模型权重可在 https://github.com/ChengpengChen/RepGhost 获取。