GRB-Sty: Redesign of Generative Residual Block for StyleGAN
Seung Park, Yong-Goo Shin · ICT Express · 2025
We have previously published a paper introducing a novel module, the Generative Residual Block (GRB), which successfully enhances GAN performance. However, the experiments in the earlier paper were conducted on baseline models using spectral normalization, a technique seldom used today. To address this problem, we investigate the effectiveness of GRB on contemporary StyleGAN-based models. This paper introduces an enhanced version of GRB, termed GRB-Sty, which consistently boosts the performance of StyleGAN-based models and demonstrates versatility across various aspects. The significant performance enhancements observed in extensive experiments on multiple benchmark datasets highlight the compatibility of GRB-Sty with state-of-the-art methods.