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This paper tackles the problem of Lipschitz regularization of Convolutional Neural Networks.
On Evaluating Adversarial Robustness
Carlini, N.; Athalye, A.; Papernot, N.; Brendel, W.; Rauber, J.; Tsipras, D.; Goodfellow, I.; and Madry, A. 2019 · 1902
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On adaptive attacks to adversarial example defenses
Tramer, F.; Carlini, N.; Brendel, W.; and Madry, A. 2020 · 2002
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Toeplitz and circulant matrices: A review
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Asymptotic Singular Value Distribution of Linear Convolutional Layers
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Finding the maximum modulus of a polynomial on the polydisk using a generalization of steckins lemma
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Block Toeplitz matrices: Asymptotic results and applications
Gutiérrez-Gutiérrez, J.; Crespo, P. M.; et al. 2012 · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012 · 2012
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Very deep convolutional networks for large-scale image recognition
Simonyan, K.; and Zisserman, A. 2014 · 2014
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Explaining and Harnessing Adversarial Examples
Goodfellow, I.; Shlens, J.; and Szegedy, C. 2015 · 2015
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A guide to convolution arithmetic for deep learning
Dumoulin, V.; and Visin, F. 2016 · 2016
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Deep residual learning for image recognition
Spectral norm regularization for improving the generalizability of deep learning
Yoshida, Y.; and Miyato, T. 2017 · 2017
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Regularisation of neural networks by enforcing lipschitz continuity
Gouk, H.; Frank, E.; Pfahringer, B.; and Cree, M. 2018 · 2018
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Towards Deep Learning Models Resistant to Adversarial Attacks
Madry, A.; Makelov, A.; Schmidt, L.; Tsipras, D.; and Vladu, A. 2018 · 2018
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Spectral normalization for generative adversarial networks
Miyato, T.; Kataoka, T.; Koyama, M.; and Yoshida, Y. 2018 · 2018
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Bounding multivariate trigonometric polynomials with applications to filter bank design
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He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and¡ 0.5 MB model size
Iandola, F. N.; Han, S.; Moskewicz, M. W.; Ashraf, K.; Dally, W. J.; and Keutzer, K. 2016 · 2016
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Zagoruyko, S.; and Komodakis, N. 2016 · 2016
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Arjovsky, M.; Chintala, S.; and Bottou, L. 2017 · 2017
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Spectrally-normalized margin bounds for neural networks
Bartlett, P. L.; Foster, D. J.; and Telgarsky, M. J. 2017 · 2017
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Towards evaluating the robustness of neural networks
Carlini, N.; and Wagner, D. 2017 · 2017
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Parseval Networks: Improving Robustness to Adversarial Examples
Cisse, M.; Bojanowski, P.; Grave, E.; Dauphin, Y.; and Usunier, N. 2017 · 2017
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Pfister, L.; and Bresler, Y. 2018 · 2018
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Adversarially robust generalization requires more data
Schmidt, L.; Santurkar, S.; Tsipras, D.; Talwar, K.; and Madry, A. 2018 · 2018
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Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks
Tsuzuku, Y.; Sato, I.; and Sugiyama, M. 2018 · 2018
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Lipschitz regularity of deep neural networks: analysis and efficient estimation
Virmaux, A.; and Scaman, K. 2018 · 2018
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Generalizable Adversarial Training via Spectral Normalization
Farnia, F.; Zhang, J.; and Tse, D. 2019 · 2019
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Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks
Fazlyab, M.; Robey, A.; Hassani, H.; Morari, M.; and Pappas, G. 2019 · 2019
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Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks
Li, Q.; Haque, S.; Anil, C.; Lucas, J.; Grosse, R. B.; and Jacobsen, J.-H. 2019 · 2019
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The Singular Values of Convolutional Layers
Sedghi, H.; Gupta, V.; and Long, P. M. 2019 · 2019
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Lipschitz constant estimation for Neural Networks via sparse polynomial optimization
Latorre, F.; Rolland, P.; and Cevher, V. 2020 · 2020
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