Fetching the paper…
Reading the bibliography…
Deep neural networks have lately shown tremendous performance in various applications including vision and speech processing tasks.
Estimating the hessian by back-propagating curvature
Martens, J., Sutskever, I., Swersky, K.: · 2012
Earlier work this paper cites.
Networks for machine learning - lecture 6a - overview of mini-batch gradient descent (2012)
Hinton, G.: · 2012
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I.J., Shlens, J., Szegedy, C.: · 2015
Earlier work this paper cites.
Tensorflow: Large-scale machine learning on heterogeneous systems, 2015
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., et al.: · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D.P., Ba, J.: · 2015
Earlier work this paper cites.
Deep Learning
Goodfellow, I., Bengio, Y., Courville, A.: · 2016
Earlier work this paper cites.
Deepfool: A simple and accurate method to fool deep neural networks
Moosavi-Dezfooli, S.M., Fawzi, A., Frossard, P.: · 2016
Earlier work this paper cites.
The limitations of deep learning in adversarial settings
Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: · 2016
Earlier work this paper cites.
Distillation as a defense to adversarial perturbations against deep neural networks
Papernot, N., McDaniel, P., Wu, X., Jha, S., Swami, A.: · 2016
Earlier work this paper cites.
Shaham, U., Yamada, Y., Negahban, S.: · 2016
Earlier work this paper cites.
Distributional smoothing with virtual adversarial training
Miyato, T., ichi Maeda, S., Koyama, M., Nakae, K., Ishii, S.: · 2016
Earlier work this paper cites.
Universal adversarial perturbations
Moosavi-Dezfooli, S.M., Fawzi, A., Fawzi, O., Frossard, P.: · 2017
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Carlini, N., Wagner, D.: · 2017
Cited alongside, same era.
On detecting adversarial perturbations
Metzen, J.H., Genewein, T., Fischer, V., Bischoff, B.: · 2017
Cited alongside, same era.
Adversarial examples detection in deep networks with convolutional filter statistics
Li, X., Li, F.: · 2017
Cited alongside, same era.
Safetynet: Detecting and rejecting adversarial examples robustly
Lu, J., Issaranon, T., Forsyth, D.: · 2017
Cited alongside, same era.
Magnet: A two-pronged defense against adversarial examples
Meng, D., Chen, H.: · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madryi, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: · 2017
Later among the works it cites.
Classification regions of deep neural networks
Fawzi, A., Moosavi-Dezfooli, S., Frossard, P., Soatto, S.: · 2017
Later among the works it cites.
Analysis of universal adversarial perturbations
Moosavi-Dezfooli, S.M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: · 2017
Later among the works it cites.
Robust large margin deep neural networks
Sokolic, J., Giryes, R., Sapiro, G., Rodrigues, M.R.D.: · 2017
Later among the works it cites.
Learning to attack: Adversarial transformation networks
Baluja, S., Fischer, I.: · 2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Feinman, R., Curtin, R.R., Shintre, S., Gardner, A.B.: · 2017
Cited alongside, same era.
Parseval networks: Improving robustness to adversarial examples
Cisse, M., Bojanowski, P., Grave, E., Dauphin, Y., Usunier, N.: · 2017
Cited alongside, same era.
Ross, A.S., Doshi-Velez, F.: · 2017
Cited alongside, same era.
Formal guarantees on the robustness of a classifier against adversarial manipulation
Hein, M., Andriushchenko, M.: · 2017
Cited alongside, same era.
Adversarial image perturbation for privacy protection a game theory perspective
Oh, S.J., Fritz, M., Schiele, B.: · 2017
Cited alongside, same era.
The space of transferable adversarial examples
Tramer, F., Papernot, N., Goodfellow, I., Boneh1, D., McDaniel, P.: · 2017
Cited alongside, same era.
Feature squeezing: Detecting adversarial examples in deep neural networks
Xu, W., Evans, D., Qi, Y.: · 2018
Closest in time.
Towards robust deep neural networks with bang
Rozsa, A., Gunther, M., E. Boult, T.: · 2018
Closest in time.
Ensemble methods as a defense to adversarial perturbations against deep neural networks
Strauss, T., Hanselmann, M., Junginger, A., Ulmer, H.: · 2018
Closest in time.
Certifying some distributional robustness with principled adversarial training
Sinha, A., Namkoong, H., Duchi, J.: · 2018
Closest in time.
Adversarial vulnerability of neural networks increases with input dimension
Simon-Gabriel, C.J., Ollivier, Y., Bottou, L., Schölkopf, B., Lopez-Paz, D.: · 2018
Closest in time.
Gradient regularization improves accuracy of discriminative models
Varga, D., Csiszarik, A., Zombori, Z.: · 2018
Closest in time.
Fix your classifier: the marginal value of training the last weight layer
Hoffer, E., Hubara, I., Soudry, D.: · 2018
Closest in time.