  In recent years, generative adversarial network (GAN)-based image generation
techniques design their generators by stacking up multiple residual blocks. The
residual block generally contains a shortcut, \ie skip connection, which
effectively supports information propagation in the network. In this paper, we
propose a novel shortcut method, called the gated shortcut, which not only
embraces the strength point of the residual block but also further boosts the
GAN performance. More specifically, based on the gating mechanism, the proposed
method leads the residual block to keep (or remove) information that is
relevant (or irrelevant) to the image being generated. To demonstrate that the
proposed method brings significant improvements in the GAN performance, this
paper provides extensive experimental results on the various standard datasets
such as CIFAR-10, CIFAR-100, LSUN, and tiny-ImageNet. Quantitative evaluations
show that the gated shortcut achieves the impressive GAN performance in terms
of Frechet inception distance (FID) and Inception score (IS). For instance, the
proposed method improves the FID and IS scores on the tiny-ImageNet dataset
from 35.13 to 27.90 and 20.23 to 23.42, respectively.
