Image Synthesis with a Single (Robust) Classifier

06/06/2019 ∙ by Shibani Santurkar, et al. ∙ 0

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



There are no comments yet.


Code Repositories


Notebooks for reproducing the paper "Computer Vision with a Single (Robust) Classifier"

view repo
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.