On Lipschitz Bounds of General Convolutional Neural Networks

DOI10.1109/TIT.2019.2961812zbMath1446.94032arXiv1808.01415OpenAlexW2997680707MaRDI QIDQ5123816

Radu Balan, Dongmian Zou, Maneesh Kumar Singh

Publication date: 29 September 2020

Published in: IEEE Transactions on Information Theory (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1808.01415

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (6)

CLIP: cheap Lipschitz training of neural networks ⋮ Stable parameterization of continuous and piecewise-linear functions ⋮ On the sensitivity of pose estimation neural networks: rotation parameterizations, Lipschitz constants, and provable bounds ⋮ Stability of the scattering transform for deformations with minimal regularity ⋮ Connections between numerical algorithms for PDEs and neural networks ⋮ Regularisation of neural networks by enforcing Lipschitz continuity

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