%0 Generic %1 liang2020towards %A Liang, Yuting %A Samavi, Reza %D 2020 %K 2020 deep-learning %T Towards Robust Deep Learning with Ensemble Networks and Noisy Layers %U http://arxiv.org/abs/2007.01507 %X In this paper we provide an approach for deep learning that protects against adversarial examples in image classification-type networks. The approach relies on two mechanisms:1) a mechanism that increases robustness at the expense of accuracy, and, 2) a mechanism that improves accuracy but does not always increase robustness. We show that an approach combining the two mechanisms can provide protection against adversarial examples while retaining accuracy. We formulate potential attacks on our approach and provide experimental results to demonstrate the effectiveness of our approach.

@misc{liang2020towards, abstract = {In this paper we provide an approach for deep learning that protects against adversarial examples in image classification-type networks. The approach relies on two mechanisms:1) a mechanism that increases robustness at the expense of accuracy, and, 2) a mechanism that improves accuracy but does not always increase robustness. We show that an approach combining the two mechanisms can provide protection against adversarial examples while retaining accuracy. We formulate potential attacks on our approach and provide experimental results to demonstrate the effectiveness of our approach.}, added-at = {2020-07-06T17:36:35.000+0200}, author = {Liang, Yuting and Samavi, Reza}, biburl = {https://www.bibsonomy.org/bibtex/22cdb0775b730b17ac053bdead95ff730/analyst}, description = {[2007.01507v1] Towards Robust Deep Learning with Ensemble Networks and Noisy Layers}, interhash = {069efb6b62b720ee7ec785ea181de727}, intrahash = {2cdb0775b730b17ac053bdead95ff730}, keywords = {2020 deep-learning}, note = {cite arxiv:2007.01507}, timestamp = {2020-07-06T17:36:35.000+0200}, title = {Towards Robust Deep Learning with Ensemble Networks and Noisy Layers}, url = {http://arxiv.org/abs/2007.01507}, year = 2020 }