Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks

3 November 2019

Joern-Henrik Jacobsen

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Papers citing "Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks"

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Title
Parseval Convolution Operators and Neural Networks Michael Unser Stanislas Ducotterd 23 3 0 19 Aug 2024
Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers Jonas Ngnawé Sabyasachi Sahoo Y. Pequignot Frédéric Precioso Christian Gagné AAML 39 0 0 26 Jun 2024
Graph Unitary Message Passing Haiquan Qiu Yatao Bian Quanming Yao 34 2 0 17 Mar 2024
A PAC-Bayesian Link Between Generalisation and Flat Minima Maxime Haddouche Paul Viallard Umut Simsekli Benjamin Guedj 40 3 0 13 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
LipSim: A Provably Robust Perceptual Similarity Metric Sara Ghazanfari Alexandre Araujo P. Krishnamurthy Farshad Khorrami Siddharth Garg 31 5 0 27 Oct 2023
Function-Space Optimality of Neural Architectures with Multivariate Nonlinearities Rahul Parhi Michael Unser 39 5 0 05 Oct 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
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
Dynamical systems' based neural networks E. Celledoni Davide Murari B. Owren Carola-Bibiane Schönlieb Ferdia Sherry OOD 40 10 0 05 Oct 2022
Provably Adversarially Robust Nearest Prototype Classifiers Václav Voráček Matthias Hein AAML 20 11 0 14 Jul 2022
Towards Evading the Limits of Randomized Smoothing: A Theoretical Analysis Raphael Ettedgui Alexandre Araujo Rafael Pinot Y. Chevaleyre Jamal Atif AAML 32 3 0 03 Jun 2022
Approximation of Lipschitz Functions using Deep Spline Neural Networks Sebastian Neumayer Alexis Goujon Pakshal Bohra M. Unser 24 15 0 13 Apr 2022
projUNN: efficient method for training deep networks with unitary matrices B. Kiani Randall Balestriero Yann LeCun S. Lloyd 36 32 0 10 Mar 2022
GraN-GAN: Piecewise Gradient Normalization for Generative Adversarial Networks Vineeth S. Bhaskara Tristan Aumentado-Armstrong Allan D. Jepson Alex Levinshtein GAN 30 5 0 04 Nov 2021
CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networks Mikhail Aleksandrovich Pautov Nurislam Tursynbek Marina Munkhoeva Nikita Muravev Aleksandr Petiushko Ivan V. Oseledets AAML 44 15 0 22 Sep 2021
Existence, Stability and Scalability of Orthogonal Convolutional Neural Networks E. M. Achour Franccois Malgouyres Franck Mamalet 16 20 0 12 Aug 2021
Deep Isometric Learning for Visual Recognition Haozhi Qi Chong You X. Wang Yi-An Ma Jitendra Malik VLM 24 53 0 30 Jun 2020
The Convolution Exponential and Generalized Sylvester Flows Emiel Hoogeboom Victor Garcia Satorras Jakub M. Tomczak Max Welling 19 28 0 02 Jun 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 220 348 0 14 Jun 2018
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 228 1,835 0 03 Feb 2017