LipKernel parameterizes dissipative convolution kernels via 2-D Roesser state-space models so that layer-wise LMIs enforce network Lipschitz bounds while allowing standard fast convolution evaluation after training.
Lipschitz- bounded 1D convolutional neural networks using the Cayley transform and the controllability Gramian,
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LipKernel: Lipschitz-Bounded Convolutional Neural Networks via Dissipative Layers
LipKernel parameterizes dissipative convolution kernels via 2-D Roesser state-space models so that layer-wise LMIs enforce network Lipschitz bounds while allowing standard fast convolution evaluation after training.