Rethinking Image Skip Connections in StyleGAN2

TL;DR

This paper provides a mathematical analysis of image skip connections in StyleGAN2, introduces a new image squeeze connection method, and demonstrates its effectiveness in improving image synthesis quality and reducing model complexity.

Contribution

It offers the first in-depth mathematical understanding of skip connections in StyleGAN2 and proposes a novel image squeeze connection that enhances performance and efficiency.

Findings

The proposed method improves image synthesis quality.

It reduces the number of network parameters needed.

Experiments show consistent performance gains across datasets.

Abstract

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.