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arXiv · 1907.11235

A Frobenius norm regularization method for convolutional kernels to avoid unstable gradient problem

Pei-Chang Guo
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Abstract

Convolutional neural network is a very important model of deep learning. It can help avoid the exploding/vanishing gradient problem and improve the generalizability of a neural network if the singular values of the Jacobian of a layer are bounded around $1$ in the training process. We propose a new penalty function for a convolutional kernel to let the singular values of the corresponding transformation matrix are bounded around $1$. We show how to carry out the gradient type methods. The penalty is about the structured transformation matrix corresponding to a convolutional kernel. This provides a new regularization method about the weights of convolutional layers.

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Pei-Chang Guo. 2019-07-25. A Frobenius norm regularization method for convolutional kernels to avoid unstable gradient problem. https://arxiv.org/abs/1907.11235

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