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We study sparsification of convolutional neural networks (CNN) by a relaxed variable splitting method of $\ell _0$ and transformed-$\ell _1$ (T$\ell _1$) penalties, with application to complex curves such as texts written in different fonts, and words written with trembling hands simulating those of Parkinson’s disease patients. The CNN contains 3 convolutional layers, each followed by a maximum pooling, and finally a fully connected layer which contains the largest number of network weights. With $\ell _0$ penalty, we achieved over 99% test accuracy in distinguishing shaky vs. regular fonts or hand writings with above 86% of the weights in the fully connected layer being zero. Comparable sparsity and test accuracy are also reached with a proper choice of T$\ell _1$ penalty.

advances in intelligent systems and computing

Learning Sparse Neural Networks via $$\ell _0$$ and T $$\ell _1$$ by a Relaxed Variable Splitting Method with Application to Multi-scale Curve Classification