Image classification is an essential component of expert and intelligent systems. The accuracy and efficiency of image classification algorithms significantly affect the performance of related expert systems. Residual network (ResNet) shows strong superiority in image modeling. However, it has also been proved to be low-efficient. In this study, we proposed a novel channel-wise attention mechanism to alleviate the redundancy of ResNet. We introduce the identity mappings into the scope of channel relationship modeling. In this way, the identity mapping can join the optimized process of self-supplementary modeling. Besides, we present the masked regularization for squeezed signals and enhance the robustness of channel-relation encoding. Finally, we verify the performance of the proposed method. The experiments are carried out on the datasets CIFAR-10, CIFAR-100, SVHN, and ImageNet. The proposed method effectively improves the performance of image classification-related expert systems. Moreover, our approach is hot-swappable, has broad applicability, so it has great practical significance for experts and intelligent systems.

图像分类是专家和智能系统的重要组成部分。图像分类算法的准确性和效率极大地影响了相关专家系统的性能。残差网络（ResNet）在图像建模方面显示出强大的优势。但是，它也被证明是低效率的。在这项研究中，我们提出了一种新颖的通道注意机制来减轻ResNet的冗余。我们将身份映射引入渠道关系建模的范围。通过这种方式，身份映射可以加入自我补充建模的优化过程。此外，我们提出了压缩信号的屏蔽正则化，并增强了信道相关编码的鲁棒性。最后，我们验证了所提方法的性能。实验在数据集CIFAR-10，CIFAR-100，SVHN和ImageNet上进行。所提出的方法有效地提高了图像分类相关专家系统的性能。而且，我们的方法是可热交换的，具有广泛的适用性，因此对专家和智能系统具有重要的现实意义。