Physical Sciences and Mathematics Commons^™

Improving Optimization Of Convolutional Neural Networks Through Parameter Fine-Tuning, Nicholas C. Becherer, John M. Pecarina, Scott L. Nykl, Kenneth M. Hopkinson

Faculty Publications

In recent years, convolutional neural networks have achieved state-of-the-art performance in a number of computer vision problems such as image classification. Prior research has shown that a transfer learning technique known as parameter fine-tuning wherein a network is pre-trained on a different dataset can boost the performance of these networks. However, the topic of identifying the best source dataset and learning strategy for a given target domain is largely unexplored. Thus, this research presents and evaluates various transfer learning methods for fine-grained image classification as well as the effect on ensemble networks. The results clearly demonstrate the effectiveness of parameter …