Source Data-less Efficient Transfer Learning Based on Layer Importance Index Using Activated Feature Map

Transfer learning performance in convolutional neural networks (CNNs) depends on the selection of retraining and fixation layers. The conventional method requires source data, which increases the cost proportionally to the data’s size. To solve this problem, this study proposed a source-data-less layer importance index that indicates the layers that should be retrained. Consequently, using the maximum activated feature maps generated by the model itself instead of the activated feature maps, the proposed method eliminates the need for any source data. Further, the experimental results on the five image classification datasets demonstrated a strong positive correlation of the proposed layer importance index compared with the conventional method. In addition, the transfer learning method using the layer importance index significantly improved the average classification accuracy compared with fine-tuning. CNNs have become indispensable owing to various applications, and thus, this study aimed at further enhancing its capabilities is relevant to the present and coming times.