In recent years, deep learning has achieved great successes in the field of image cognitive learning, and designing a well-behaved convolutional neural network (CNN)-based architecture has become a challenging and important problem. The traditional group convolution cannot effectively address the severe problem of “information blocking”; hence, this work proposes an efficient CNN-based model to achieve an effective exchange of information between channels. A novel Group Sequential (GS) learning that uses a channel split operation and sequential learning methods is introduced to improve the recognition performance by increasing information communication. Several state-of-the-art models are developed based on GS blocks, and these blocks significantly boost the performance and robustness of the CNN-based models. Extensive experiments are carried out to evaluate the promising performance of the proposed GSNet framework, and experimental results show its superiority on several benchmarks (i.e., the CIFAR-10, CIFAR-100, Tiny ImageNet, ImageNet, and FOOD-101 dataset). Moreover, compared with traditional residual networks (e.g., ResNet-101), the proposed network has achieved a great improvement with fewer parameters, and the error rate of models on the FOOD-101 dataset decreases from 19.08 to 16.02%. The proposed GS block method has significant potential to improve the performance for image recognition, and advance the development of cognitive computation. The results demonstrate the superiority of the proposed method and indicate excellent generalization ability. Code is available at: https://github.com/shao15xiang/GSNet.

近年来，深度学习在图像认知学习领域取得了巨大的成功，设计基于行为良好的卷积神经网络（CNN）的体系结构已成为具有挑战性和重要的问题。传统的群卷积无法有效解决“信息阻塞”这一严重问题。因此，这项工作提出了一个有效的基于CNN的模型，以实现频道之间信息的有效交换。引入了一种新颖的使用序列分割操作和顺序学习方法的组顺序学习（GS）学习，以通过增加信息交流来提高识别性能。基于GS块开发了几种最新模型，这些块显着提高了基于CNN的模型的性能和鲁棒性。进行了广泛的实验以评估提出的GSNet框架的有希望的性能，并且实验结果显示了它在几个基准（即CIFAR-10，CIFAR-100，Tiny ImageNet，ImageNet和FOOD-101数据集）上的优越性。此外，与传统的残差网络（例如ResNet-101）相比，所提出的网络以较少的参数获得了很大的改进，并且FOOD-101数据集上模型的错误率从19.08％降低到16.02％。提出的GS块方法在提高图像识别性能，促进认知计算发展方面具有巨大潜力。结果证明了所提方法的优越性，并指出了优良的泛化能力。可以从以下网址获得代码：https：//github.com/shao15xiang/GSNet。