Rethinking Clustering for Robustness


This paper studies how encouraging semantically-aligned features during deep neural network training can increase network robustness. Recent works observed that Adversarial Training leads to robust models, whose learnt features appear to correlate with human perception. Inspired by this connection from robustness to semantics, we study the complementary connection, from semantics to robustness. To do so, we provide a robustness certificate for distance-based classification models (clustering-based classifiers). Moreover, we show that this certificate is tight, and we leverage it to propose ClusTR (Clustering Training for Robustness), a clustering-based and adversary-free training framework to learn robust models.

In British Machine Vision Conference
Motasem Alfarra
Motasem Alfarra
PhD Candidate - Electrical and Computer Engineering

I am a Ph.D. candidate at KAUST in Saudi Arabia. I am part of the Image and Video Understanding Lab (IVUL) advised by Prof. Bernard Ghanem. I obtained my M.Sc degree in Electrical Engineering from KAUST, and my undergraduate degree in Electrical Engineering from Kuwait University. I am interested in building robust neural networks and leveraging robust models for different applications.