  The generator in the generative adversarial network (GAN) learns image
generation in a coarse-to-fine manner in which earlier layers learn the overall
structure of the image and the latter ones refine the details. To propagate the
coarse information well, recent works usually build their generators by
stacking up multiple residual blocks. Although the residual block can produce a
high-quality image as well as be trained stably, it often impedes the
information flow in the network. To alleviate this problem, this brief
introduces a novel generator architecture that produces the image by combining
features obtained through two different branches: the main and auxiliary
branches. The goal of the main branch is to produce the image by passing
through the multiple residual blocks, whereas the auxiliary branch is to convey
the coarse information in the earlier layer to the later one. To combine the
features in the main and auxiliary branches successfully, we also propose a
gated feature fusion module that controls the information flow in those
branches. To prove the superiority of the proposed method, this brief provides
extensive experiments using various standard datasets including CIFAR-10,
CIFAR-100, LSUN, CelebA-HQ, AFHQ, and tiny-ImageNet. Furthermore, we conducted
various ablation studies to demonstrate the generalization ability of the
proposed method. Quantitative evaluations prove that the proposed method
exhibits impressive GAN performance in terms of Inception score (IS) and
Frechet inception distance (FID). For instance, the proposed method boosts the
FID and IS scores on the tiny-ImageNet dataset from 35.13 to 25.00 and 20.23 to
25.57, respectively.
