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.

本研究提出了一种新的单元结构 - 特征循环块（FCB），可以用于生成对抗网络（GANs）的生成器架构中。该单元结构具有两个分支，一个是内存分支，另一个是图像分支。 其中，内存分支在生成器的每个阶段保持有意义的信息，图像分支则从内存分支获取一些有用的特征，以生成高质量的图像。 通过在多个数据集上进行实验，结果表明了本方法的显著优越性。