  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.
