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  • Learning Sparse Neural Networks via ℓ0 and Tℓ1 by a Relaxed Variable Splitting Method with Application to Multi-scale Curve Classification
Title: Learning Sparse Neural Networks via ℓ0 and Tℓ1 by a Relaxed Variable Splitting Method with Application to Multi-scale Curve Classification
We study sparsification of convolutional neural networks (CNN) by a relaxed variable splitting method of ℓ0 and transformed-ℓ1 (Tℓ1) 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 ℓ0 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 Tℓ1 penalty.  more » « less
Award ID(s):
1632935
PAR ID:
10112757
Author(s) / Creator(s):
;
Date Published:
Journal Name:
6th World Congress on Global Optimization
Page Range / eLocation ID:
800-809
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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