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.
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Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report (2009)
Bengio, Y.: Deep learning of representations for unsupervised and transfer learning. In: JMLR W&CP: Proceedings of Unsupervised and Transfer Learning (2011)
Bengio, Y., Bastien, F., Bergeron, A., Boulanger-Lew, N., Breuel, T., Chherawala, Y., Cisse, M., Côté, M., Erhan, D., Eustache, J., Glorot, X., Muller, X., Lebeuf, S.P., Pascanu, R., Rifai, S., Savard, F., Sicard, G.: Deep learners benefit more from out-of-distribution examples. In: JMLR W&CP: Proceedings of AISTATS 2011 (2011)
Caruana, R.: Learning many related tasks at the same time with backpropagation. In: Advances in Neural Information Processing Systems, pp. 657–664. Morgan Kaufmann (1995)
Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: Computer Vision and Pattern Recognition. IEEE (2009)
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: DeCAF: A deep convolutional activation feature for generic visual recognition. CoRR abs/1310.1531 (2013)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Computer Vision and Pattern Recognition. IEEE (2014)
Graham, B.: Fractional max-pooling. CoRR abs/1412.6071 (2015)
Hu, F., Xia, G.S., Hu, J., Zhang, L.: Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sens. 7(11), 14680–14707 (2015)
Mehdipour Ghazi, M., Yanikoglu, B., Aptoula, E.: Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing 235, 228–235 (2017)
Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014. IEEE (2014)
Razavian, A.S., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: an astounding baseline for recognition. In: Computer Vision and Pattern Recognition Workshops, CVPRW 2014. IEEE (2014)
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., Lecun, Y.: OverFeat: integrated recognition, localization and detection using convolutional networks. In: International Conference on Learning Representations (2014)
Shin, H., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35, 1285–1298 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Szegedy, C., et al.: Going deeper with convolutions. In: Computer Vision and Pattern Recognition. IEEE (2015)
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks?. In: Advances in Neural Information Processing Systems (2014)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). doi:10.1007/978-3-319-10590-1_53
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Zhi, W., Chen, Z., Yueng, H.W.F., Lu, Z., Zandavi, S.M., Chung, Y.Y. (2017). Layer Removal for Transfer Learning with Deep Convolutional Neural Networks. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10635. Springer, Cham. https://doi.org/10.1007/978-3-319-70096-0_48
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DOI: https://doi.org/10.1007/978-3-319-70096-0_48
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