  Despite recent advances, large scale visual artifacts are still a common
occurrence in images generated by GANs. Previous work has focused on improving
the generator's capability to accurately imitate the data distribution
$p_{data}$. In this paper, we instead explore methods that enable GANs to
actively avoid errors by manipulating the input space. The core idea is to
apply small changes to each noise vector in order to shift them away from areas
in the input space that tend to result in errors. We derive three different
architectures from that idea. The main one of these consists of a simple
residual module that leads to significantly less visual artifacts, while only
slightly decreasing diversity. The module is trivial to add to existing GANs
and costs almost zero computation and memory.
