Recent work has shown that Binarized Neural Networks (BNNs) are able to greatly reduce computational costs and memory footprints, facilitating model deployment on resource-constrained devices. However, in comparison to their full-precision counterparts, BNNs suffer from severe accuracy degradation. Research aiming to reduce this accuracy gap has thus far largely focused on specific network architectures with few or no 1x1 convolutional layers, for which standard binarization methods do not work well. Because 1x1 convolutions are common in the design of modern architectures (e.g. GoogleNet, ResNet, DenseNet), it is crucial to develop a method to binarize them effectively for BNNs to be more widely adopted. In this work, we propose an "Elastic-Link" (EL) module to enrich information flow within a BNN by adaptively adding real-valued input features to the subsequent convolutional output features. The proposed EL module is easily implemented and can be used in conjunction with other methods for BNNs. We demonstrate that adding EL to BNNs produces a significant improvement on the challenging large-scale ImageNet dataset. For example, we raise the top-1 accuracy of binarized ResNet26 from 57.9% to 64.0%. EL also aids convergence in the training of binarized MobileNet, for which a top-1 accuracy of 56.4% is achieved. Finally, with the integration of ReActNet, it yields a new state-of-the-art result of 71.9% top-1 accuracy.

翻译：近期研究表明，二值化神经网络（BNNs）能够显著降低计算开销与内存占用，有助于在资源受限设备上部署模型。然而，与全精度网络相比，BNNs存在严重的精度下降问题。现有旨在缩小精度差距的研究大多聚焦于特定网络架构（如无或仅含少量1x1卷积层的结构），而标准二值化方法在此类架构上效果不佳。由于1x1卷积在现代网络设计（如GoogleNet、ResNet、DenseNet）中十分普遍，开发一种有效二值化此类卷积的方法对推广BNNs至关重要。本文提出一种“弹性连接”（Elastic-Link, EL）模块，通过自适应地将实值输入特征叠加至后续卷积输出特征，以增强BNN内部的信息流动。所提出的EL模块易于实现，并能与其他BNN方法协同使用。实验表明，在具有挑战性的大规模ImageNet数据集上，将EL引入BNNs能显著提升性能。例如，我们将二值化ResNet26的Top-1准确率从57.9%提升至64.0%。EL也有助于二值化MobileNet的训练收敛，使其达到56.4%的Top-1准确率。最终，结合ReActNet后，该方法取得了71.9% Top-1准确率的新最优结果。