###### Abstract  
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. M +
Footnote †: footnote  
In recent years, the escalating computational capabilities of deep learning models have spurred the development of novel architectures that preliminarily mitigate the issue of feature vanishing. Among these, U-Net [13] and its enhanced variant, U-Net++ [14], stand out as the most prominent examples. Originally introduced by [PERSON] et al. in 2015 [13], U-Net was initially designed for biomedical image segmentation. Its encoder-decoder architecture [13] adeptly integrates deep feature extraction with precise image reconstruction, enabling exceptional performance across a wide range of image segmentation tasks. Building on this foundation, subsequent research efforts led to the development of U-Net++, which incorporates additional skip connections [15] and a modular design. These innovations significantly improve the model's ability to process fine details and fuse features effectively. However, the skip connections in U-Net and U-Net++ are confined to linking features at the same hierarchical levels. As the encoder performs downsampling, critical features may be lost, hindering their effective propagation to subsequent layers. To address this challenge, we propose an Asymmetric Skip Connection (ASC) mechanism. Unlike traditional skip connections, our ASC mechanism offsets connections forward from the same level, enabling the direct integration of shallow encoder features with deeper, more abstract features. Related research highlights [16; 17; 18; 19] that homogeneous ensemble learning offers a viable approach to address limitations in accuracy for specific tasks [18]. By aggregating the predictions of multiple models of the same type, ensemble learning effectively mitigates the bias and variance inherent in individual models, thereby enhancing overall performance. Compared to heterogeneous ensemble learning, homogeneous ensemble learning exhibits a notable limitation: its reduced resistance to overfitting [20]. This vulnerability arises because homogeneous ensemble models, sharing identical architectures, are prone to converging on similar local optima during training [21]. To enhance model flexibility and counteract overfitting, we propose a Misaligned Merging Mechanism (MMM). This mechanism introduces misaligned connections between different encoder and decoder models, allowing outputs from encoders linked to distinct decoders to be merged. By disrupting the uniformity of connections, the MMM reduces the likelihood of base models converging on identical local optima, thereby bolstering the model's resistance to overfitting. Rooted in homogeneous ensemble learning, this approach addresses the low correlation among base networks, effectively harnessing the strengths of diverse base networks while mitigating information loss caused by excessive downsampling in single encoder-decoder architectures.