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Image Synthesis with a Single (Robust) Classifier
Shibani Santurkar · Andrew Ilyas · Dimitris Tsipras · Logan Engstrom · Brandon Tran · Aleksander Madry

Wed Dec 11 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #81

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.

Author Information

Shibani Santurkar (MIT)
Andrew Ilyas (MIT)
Dimitris Tsipras (MIT)
Logan Engstrom (MIT)
Brandon Tran (Massachusetts Institute of Technology)
Aleksander Madry (MIT)

Aleksander Madry is the NBX Associate Professor of Computer Science in the MIT EECS Department and a principal investigator in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). He received his PhD from MIT in 2011 and, prior to joining the MIT faculty, he spent some time at Microsoft Research New England and on the faculty of EPFL. Aleksander's research interests span algorithms, continuous optimization, science of deep learning and understanding machine learning from a robustness perspective. His work has been recognized with a number of awards, including an NSF CAREER Award, an Alfred P. Sloan Research Fellowship, an ACM Doctoral Dissertation Award Honorable Mention, and 2018 Presburger Award.

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