Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100

Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100

5 August 2021

Papers citing "Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100"

14 / 14 papers shown

Title
Monotone, Bi-Lipschitz, and Polyak-Lojasiewicz Networks Ruigang Wang Krishnamurthy Dvijotham I. Manchester 33 5 0 02 Feb 2024
1-Lipschitz Neural Networks are more expressive with N-Activations Bernd Prach Christoph H. Lampert AAML FAtt 24 0 0 10 Nov 2023
Certified Robustness via Dynamic Margin Maximization and Improved Lipschitz Regularization Mahyar Fazlyab Taha Entesari Aniket Roy Ramalingam Chellappa AAML 16 11 0 29 Sep 2023
Certified Robust Models with Slack Control and Large Lipschitz Constants M. Losch David Stutz Bernt Schiele Mario Fritz 14 4 0 12 Sep 2023
Robust low-rank training via approximate orthonormal constraints Dayana Savostianova Emanuele Zangrando Gianluca Ceruti Francesco Tudisco 24 9 0 02 Jun 2023
Towards Large Certified Radius in Randomized Smoothing using Quasiconcave Optimization Bo-Han Kung Shang-Tse Chen AAML 19 0 0 01 Feb 2023
Improved techniques for deterministic l2 robustness Sahil Singla S. Feizi AAML 23 9 0 15 Nov 2022
Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions Stanislas Ducotterd Alexis Goujon Pakshal Bohra Dimitris Perdios Sebastian Neumayer M. Unser 35 12 0 28 Oct 2022
Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks Bernd Prach Christoph H. Lampert 32 35 0 05 Aug 2022
Provably Adversarially Robust Nearest Prototype Classifiers Václav Voráček Matthias Hein AAML 20 11 0 14 Jul 2022
On the Number of Regions of Piecewise Linear Neural Networks Alexis Goujon Arian Etemadi M. Unser 44 13 0 17 Jun 2022
Approximation of Lipschitz Functions using Deep Spline Neural Networks Sebastian Neumayer Alexis Goujon Pakshal Bohra M. Unser 27 15 0 13 Apr 2022
SoK: Certified Robustness for Deep Neural Networks Linyi Li Tao Xie Bo-wen Li AAML 25 128 0 09 Sep 2020
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks Lechao Xiao Yasaman Bahri Jascha Narain Sohl-Dickstein S. Schoenholz Jeffrey Pennington 224 348 0 14 Jun 2018