Abstract | ||
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The Lipschitz constant of neural networks has been established as a key quantity to enforce the robustness to adversarial examples. In this paper, we tackle the problem of building $1$-Lipschitz Neural Networks. By studying Residual Networks from a continuous time dynamical system perspective, we provide a generic method to build $1$-Lipschitz Neural Networks and show that some previous approaches are special cases of this framework. Then, we extend this reasoning and show that ResNet flows derived from convex potentials define $1$-Lipschitz transformations, that lead us to define the Convex Potential Layer (CPL). A comprehensive set of experiments on several datasets demonstrates the scalability of our architecture and the benefits as an $\ell_2$-provable defense against adversarial examples. Our code is available at \url{https://github.com/MILES-PSL/Convex-Potential-Layer} |
Year | Venue | DocType |
---|---|---|
2022 | International Conference on Machine Learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Meunier, Laurent | 1 | 2 | 2.05 |
Blaise Delattre | 2 | 0 | 0.34 |
Alexandre Araujo | 3 | 3 | 2.05 |
Alexandre Allauzen | 4 | 323 | 45.19 |