no code implementations • 1 Jul 2022 • Maximilian Kaufmann, Yiren Zhao, Ilia Shumailov, Robert Mullins, Nicolas Papernot
In this paper we demonstrate data pruning-a method for increasing adversarial training efficiency through data sub-sampling. We empirically show that data pruning leads to improvements in convergence and reliability of adversarial training, albeit with different levels of utility degradation.
