Fine-Tuning of Pre-Trained Deep Learning Models with Extreme Learning Machine
Tagrid Abdullah N. Alshalali, Darsana P. Josyula · 2018
Transfer learning allows exploiting what was learned in one situation for faster learning in another situation. It is widely used for object recognition and image classification applications through pre-trained Convolutional Neural Networks models. Different techniques to fine-tune a pre-trained CNN and when to apply each technique are subjects of great research interest. The impact of each technique on the training time as well as test-set accuracy, influences how transfer learning is utilized. In this paper, we evaluate the performance of Extreme Learning Machine verses Fully-Connected layers on overall-training time and testing accuracy, when transfer learning is employed.