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We show that the basic classification framework alone can be used to tacklesome of the most challenging tasks in image synthesis. In contrast to otherstate-of-the-art approaches, the toolkit we develop is rather minimal: it usesa single, off-the-shelf classifier for all these tasks. The crux of ourapproach is that we train this classifier to be adversarially robust. It turnsout that adversarial robustness is precisely what we need to directlymanipulate salient features of the input. Overall, our findings demonstrate theutility of robustness in the broader machine learning context. Code and modelsfor our experiments can be found at https://git.io/robust-apps.

Image Synthesis with a Single (Robust) Classifier