Deep Transfer Learning Ensemble for Classification

Transfer learning algorithms typically assume that the training data and the test data come from different distribution. It is better at adapting to learn new tasks and concepts more quickly and accurately by exploiting previously gained knowledge. Deep Transfer Learning (DTL) emerged as a new paradigm in transfer learning in which a deep model offer greater flexibility in extracting high-level features. DTL offers selective layer based transference, and it is problem specific. In this paper, we propose the Ensemble of Deep Transfer Learning (EDTL) methodology to reduce the impact of selective layer based transference and provide optimized framework to work for three major transfer learning cases. Empirical results on character, object and biomedical image recognition tasks achieves that the proposed method indicate statistically significant classification accuracy over the other established transfer learning method.