  A common issue of deep neural networks-based methods for the problem of
Single Image Super-Resolution (SISR), is the recovery of finer texture details
when super-resolving at large upscaling factors. This issue is particularly
related to the choice of the objective loss function. In particular, recent
works proposed the use of a VGG loss which consists in minimizing the error
between the generated high resolution images and ground-truth in the feature
space of a Convolutional Neural Network (VGG19), pre-trained on the very
"large" ImageNet dataset. When considering the problem of super-resolving
images with a distribution "far" from the ImageNet images distribution
(\textit{e.g.,} satellite images), their proposed \textit{fixed} VGG loss is no
longer relevant. In this paper, we present a general framework named
\textit{Generative Collaborative Networks} (GCN), where the idea consists in
optimizing the \textit{generator} (the mapping of interest) in the feature
space of a \textit{features extractor} network. The two networks (generator and
extractor) are \textit{collaborative} in the sense that the latter "helps" the
former, by constructing discriminative and relevant features (not necessarily
\textit{fixed} and possibly learned \textit{mutually} with the generator). We
evaluate the GCN framework in the context of SISR, and we show that it results
in a method that is adapted to super-resolution domains that are "far" from the
ImageNet domain.
