  Although CNNs have gained the ability to transfer learned knowledge from
source task to target task by virtue of large annotated datasets but consume
huge processing time to fine-tune without GPU. In this paper, we propose a new
computationally efficient transfer learning approach using classification layer
features of pre-trained CNNs by appending layer after existing classification
layer. We demonstrate that fine-tuning of the appended layer with existing
classification layer for new task converges much faster than baseline and in
average outperforms baseline classification accuracy. Furthermore, we execute
thorough experiments to examine the influence of quantity, similarity, and
dissimilarity of training sets in our classification outcomes to demonstrate
transferability of classification layer features.
