REAF: Reducing Approximation of Channels by Reducing Feature Reuse Within Convolution

High-level feature maps of Convolutional Neural Networks are computed by reusing their corresponding low-level feature maps, which brings into full play feature reuse to improve the computational efficiency. This form of feature reuse is referred to as feature reuse between convolutional layers. The second type of feature reuse is referred to as feature reuse within the convolution, where the channels of the output feature maps of the convolution are computed by reusing the same channels of the input feature maps, which results in an approximation of the channels of the output feature maps. To compute them accurately, we need specialized input feature maps for every channel of the output feature maps. In this paper, we first discuss the approximation problem introduced by full feature reuse within the convolution and then propose a new feature reuse scheme called Reducing Approximation of channels by Reducing Feature reuse (REAF). The paper also shows that group convolution is a special case of our REAF scheme and we analyze the advantage of REAF compared to such group convolution. Moreover, we develop the REAF+ scheme and integrate it with group convolution-based models. Compared with baselines, experiments on image classification demonstrate the effectiveness of our REAF and REAF+ schemes. Under the given computational complexity budget, the Top-1 accuracy of REAF-ResNet50 and REAF+-MobileNetV2 on ImageNet will increase by 0.37% and 0.69% respectively. The code and pre-trained models will be publicly available.