Generative adversarial networks (GANs), built with a generator and discriminator, significantly have advanced image generation. Typically, existing papers build their generators by stacking up multiple residual blocks since it makes ease the training of generators. However, some recent papers commented on the limitation of the residual block and proposed a new architectural unit that improves the GANs performance. Following this trend, this paper presents a novel unit, called feature cycling block (FCB), which achieves impressive results in the image generation task. Specifically, the FCB has two branches: one is a memory branch and the other is an image branch. The memory branch keeps meaningful information at each stage of the generator, whereas the image branch takes some useful features from the memory branch to produce a high-quality image. To show the capability of the proposed method, we conducted extensive experiments using various datasets including CIFAR-10, CIFAR-100, FFHQ, AFHQ, and subsets of LSUN. Experimental results demonstrate the substantial superiority of our approach over the baseline without incurring any objective functions or training skills. For instance, the proposed method improves Frechet inception distance (FID) of StyleGAN2 from 4.89 to 3.72 on the FFHQ dataset and from 6.64 to 5.57 on the LSUN Bed dataset. We believe that the pioneering attempt presented in this paper could inspire the community with better-designed generator architecture and with training objectives or skills compatible with the proposed method.

翻译：生成对抗网络（GAN）由生成器和判别器构成，在图像生成领域取得了显著进展。现有工作通常通过堆叠多个残差模块构建生成器，这有助于简化生成器的训练过程。然而，近期部分研究指出了残差模块的局限性，并提出了能够提升生成对抗网络性能的新型架构单元。沿袭这一趋势，本文提出一种名为特征循环模块（FCB）的新型单元，在图像生成任务中取得了令人瞩目的成果。具体而言，FCB包含两个分支：记忆分支与图像分支。记忆分支在生成器的每个阶段保留有效信息，图像分支则从记忆分支中提取关键特征以生成高质量图像。为验证所提方法的有效性，我们在包含CIFAR-10、CIFAR-100、FFHQ、AFHQ及LSUN子集在内的多个数据集上进行了大量实验。实验结果表明，本方法在不引入任何目标函数或训练技巧的情况下，显著优于基线模型。例如，所提方法将StyleGAN2在FFHQ数据集上的Fréchet起始距离（FID）从4.89降至3.72，在LSUN Bed数据集上从6.64降至5.57。我们相信，本文提出的开创性尝试将通过优化生成器架构设计，并兼容适配的训练目标或方法，为学界带来启发。