A CNN-MLP hybrid architecture for visual model robustness

In recent years, with the emergence of models based on Transformers and MLPs, such as Vision Transformer (ViT) and MLP-Mixer, researchers have begun to explore the potential of these new architectures in visual tasks. These models have achieved significant results, showing competitive performance compared to traditional CNNs. However, the robustness of models is another key research topic nowadays. To comprehensively evaluate the robustness of these models, researchers conducted extensive experiments on datasets including MNIST, Fashion-MNIST, and Fruit. Inspired by the above analysis, researchers proposed the MC (MLP-CNN) model, a hybrid architecture that combines the advantages of visual MLPs and convolutional neural networks. Experimental results show that the MC model demonstrated superior robustness across multiple datasets under adversarial attacks. These findings provide important references for designing more robust deep learning models and point the direction for future research in the field of image processing.