  Super-resolution is an ill-posed problem, where a ground-truth
high-resolution image represents only one possibility in the space of plausible
solutions. Yet, the dominant paradigm is to employ pixel-wise losses, such as
L_1, which drive the prediction towards a blurry average. This leads to
fundamentally conflicting objectives when combined with adversarial losses,
which degrades the final quality. We address this issue by revisiting the L_1
loss and show that it corresponds to a one-layer conditional flow. Inspired by
this relation, we explore general flows as a fidelity-based alternative to the
L_1 objective. We demonstrate that the flexibility of deeper flows leads to
better visual quality and consistency when combined with adversarial losses. We
conduct extensive user studies for three datasets and scale factors, where our
approach is shown to outperform state-of-the-art methods for photo-realistic
super-resolution. Code and trained models will be available at:
  git.io/AdFlow
