Towards Stable and Efficient Training of Verifiably Robust Neural Networks

ICLR 2020 Huan ZhangHongge ChenChaowei XiaoSven GowalRobert StanforthBo LiDuane BoningCho-Jui Hsieh

Training neural networks with verifiable robustness guarantees is challenging. Several existing approaches utilize linear relaxation based neural network output bounds under perturbation, but they can slow down training by a factor of hundreds depending on the underlying network architectures... (read more)

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