Rethinking Image Skip Connections in StyleGAN2
This work addresses a foundational issue in image synthesis for researchers and practitioners, offering a novel method that enhances state-of-the-art models, though it is incremental as it builds upon existing StyleGAN frameworks.
The paper tackled the lack of mathematical analysis for image skip connections in StyleGAN2 by introducing an image squeeze connection, which improved image synthesis quality and reduced network parameters, as demonstrated through experiments on various datasets.
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.