Retrain-free fully connected layer optimization using matrix factorization

作者：Yi Sun, Xuejiao Liu, Luhong Liang · 年份：2017 · DOI：10.1109/icip.2017.8297016 · 被引用次数：3 · 研究领域：Advanced Neural Network Applications、Advanced Image and Video Retrieval Techniques、Brain Tumor Detection and Classification

The complexity of Deep Neural Networks (DNNs) hinders their implementation on embedded system with limited hardware resources. To deal with this issue, this paper presents a novel optimization algorithm based on semi-Nonnegative Matrix Factorization for Fully Connected layers (semi-NMF-based FC optimization). Compared with previous network surgery techniques, our proposed method optimizes network structure in a more implementation-friendly manner with full controllability and no requirement on retraining using training data. Simulations using AlexNet and VGG-16 on ImageNet task show the effectiveness and efficiency of semi-NMF-based FC optimization.