Comparative analysis of deep network models through transfer learning

Deep learning has had remarkable success in several applications such as classification, clustering, regression etc. Several assumptions are made during the learning process which may not be apt for all real-world applications due to change in the feature space. For the classification task, deep learning models are most appropriate if a large amount of data is used for training. Therefore, enhancement is made from deep learning to transfer learning by knowledge transfer from feature space. In this paper, the accuracy obtained, number of iterations, and time taken for classification of various pre-trained networks is compared through transfer learning. The results reveal that the accuracy is higher when the training data is large compared to that with a small dataset.