Thanks to their remarkable generative capabilities, GANs have gained great
popularity, and are used abundantly in state-of-the-art methods and
applications. In a GAN based model, a discriminator is trained to learn the
real data distribution. To date, it has been used only for training purposes,
where it's utilized to train the generator to provide real-looking outputs. In
this paper we propose a novel method that makes an explicit use of the
discriminator in test-time, in a feedback manner in order to improve the
generator results. To the best of our knowledge it is the first time a
discriminator is involved in test-time. We claim that the discriminator holds
significant information on the real data distribution, that could be useful for
test-time as well, a potential that has not been explored before.
The approach we propose does not alter the conventional training stage. At
test-time, however, it transfers the output from the generator into the
discriminator, and uses feedback modules (convolutional blocks) to translate
the features of the discriminator layers into corrections to the features of
the generator layers, which are used eventually to get a better generator
result. Our method can contribute to both conditional and unconditional GANs.
As demonstrated by our experiments, it can improve the results of
state-of-the-art networks for super-resolution, and image generation.

本文提出了一种新颖的方法，在测试时显式利用判别器的反馈模块并结合卷积块进行图像生成，旨在提升生成器的结果，实验结果表明这种方法可应用于超分辨率和图像生成领域并提高现有网络的性能。