  We show that the basic classification framework alone can be used to tackle
some of the most challenging tasks in image synthesis. In contrast to other
state-of-the-art approaches, the toolkit we develop is rather minimal: it uses
a single, off-the-shelf classifier for all these tasks. The crux of our
approach is that we train this classifier to be adversarially robust. It turns
out that adversarial robustness is precisely what we need to directly
manipulate salient features of the input. Overall, our findings demonstrate the
utility of robustness in the broader machine learning context. Code and models
for our experiments can be found at https://git.io/robust-apps.
