Domain Adaptation and Transfer Learning in StochasticNets

Abstract

Transfer learning is a recent field of machine learning research that
aims to resolve the challenge of dealing with insufficient training
data in the domain of interest. This is a particular issue with traditional
deep neural networks where a large amount of training
data is needed. Recently, StochasticNets was proposed to take
advantage of sparse connectivity in order to decrease the number
of parameters that needs to be learned, which in turn may relax
training data size requirements. In this paper, we study the efficacy
of transfer learning on StochasticNet frameworks. Experimental results
show 7% improvement on StochasticNet performance when
the transfer learning is applied in training step.