Residual units are wildly used for alleviating optimization difficulties when building deep neural networks. However, the performance gain does not well compensate the model size increase, indicating low parameter efficiency in these residual units. In this work, we first revisit the residual function in several variations of residual units and demonstrate that these residual functions can actually be explained with a unified framework based on generalized block term decomposition. Then, based on the new explanation, we propose a new architecture, Collective Residual Unit (CRU), which enhances the parameter efficiency of deep neural networks through collective tensor factorization. CRU enables knowledge sharing across different residual units using shared factors. Experimental results show that our proposed CRU Network demonstrates outstanding parameter efficiency, achieving comparable classification performance to ResNet-200 with the model size of ResNet-50. By building a deeper network using CRU, we can achieve state-of-the-art single model classification accuracy on ImageNet-1k and Places365-Standard benchmark datasets. (Code and trained models are available on GitHub)

本文提出了一种新的Collective Residual Unit(CRU)网络架构，通过采用共享因子的集体张量分解来增强深层神经网络的参数效率。CRU网络实现了跨不同残差单元的知识共享，实验证明其参数效率显著，达到了ResNet-200的分类性能。在ImageNet-1k和Places365-Standard基准数据集上，通过使用CRU构建更深的网络，可以实现最新的单模型分类精度。(代码和训练模型可在GitHub上找到)

通过集体张量分解在深度神经网络中共享残差单元