Football Net: Leveraging the Structure of Truncated Icosahedron in Convolutional Neural Network Design

Deep neural networks often suffer from the degradation of fine-grained features during feature transmission. To mitigate this issue, we propose an innovative CNN architecture, Football Net, which is designed to enhance feature propagation. By introducing Asymmetric Skip Connections, Football Net effectively captures and preserves fine-grained details. Another significant challenge in deep neural networks is achieving robustness. Football Net addresses this challenge by incorporating a novel misaligned feature merging mechanism and a new homogeneous ensemble learning strategy. The experimental results indicate that this improved ensemble strategy significantly reduces both bias and variance, thereby enhancing overall classification performance. We conducted extensive experiments on the CIFAR-10, ImageNet-100, and ImageNet-1k datasets. The results demonstrate the competitiveness of Football Net in image classification tasks, achieving accuracy comparable to state-of-the-art models such as ResNet, U-Net, and U-Net++ while also improving robustness.