Layer Removal for Transfer Learning with Deep Convolutional Neural Networks

Abstract

It is usually difficult to find datasets of sufficient size to train Deep Convolutional Neural Networks (DCNNs) from scratch. In practice, a neural network is often pre-trained on a very large source dataset. Then, a target dataset is transferred onto the neural network. This approach is a form of transfer learning, and allows very deep networks to achieve outstanding performance even when a small target dataset is available. It is thought that the bottom layers of the pre-trained network contain general information, which are applicable to different datasets and tasks, while the upper layers of the pre-trained network contain abstract information relevant to a specific dataset and task. While studies have been conducted on the fine-tuning of these layers, the removal of these layers have not yet been considered. This paper explores the effect of removing the upper convolutional layers of a pre-trained network. We empirically investigated whether removing upper layers of a deep pre-trained network can improve performance for transfer learning. We found that removing upper pre-trained layers gives a significant boost in performance, but the ideal number of layers to remove depends on the dataset. We suggest removing pre-trained convolutional layers when applying transfer learning on off-the-shelf pre-trained DCNNs. The ideal number of layers to remove will depend on the dataset, and remain as a parameter to be tuned.