Various models based on StyleGAN have gained significant traction in the field of image synthesis, attributed to their robust training stability and superior performances. Within the StyleGAN framework, the adoption of image skip connection is favored over the traditional residual connection. However, this preference is just based on empirical observations; there has not been any in-depth mathematical analysis on it yet. To rectify this situation, this brief aims to elucidate the mathematical meaning of the image skip connection and introduce a groundbreaking methodology, termed the image squeeze connection, which significantly improves the quality of image synthesis. Specifically, we analyze the image skip connection technique to reveal its problem and introduce the proposed method which not only effectively boosts the GAN performance but also reduces the required number of network parameters. Extensive experiments on various datasets demonstrate that the proposed method consistently enhances the performance of state-of-the-art models based on StyleGAN. We believe that our findings represent a vital advancement in the field of image synthesis, suggesting a novel direction for future research and applications.

通过分析图像跳跃连接技术的问题，并引入图像挤压连接的方法，不仅有效提升了基于StyleGAN的生成对抗网络的性能，还减少了所需的网络参数数量。大量实验证明，该方法持续提高了基于StyleGAN的最先进模型的性能，从而在图像合成领域取得了关键性进展，并为未来的研究和应用提供了新方向。

重新思考StyleGAN2中的图像跳跃连接