Input Fast-Forwarding for Better Deep Learning

Preprint English OPEN
Ibrahim, Ahmed ; Abbott, A. Lynn ; Hussein, Mohamed E. (2017)
  • Subject: Computer Science - Computer Vision and Pattern Recognition

This paper introduces a new architectural framework, known as input fast-forwarding, that can enhance the performance of deep networks. The main idea is to incorporate a parallel path that sends representations of input values forward to deeper network layers. This sche... View more
  • References (20)
    20 references, page 1 of 2

    1. BVLC reference Ca eNet model. https://github.com/BVLC/caffe/tree/ master/models/bvlc_reference_caffenet, accessed: June 2016

    2. Chetlur, S., Woolley, C., Vandermersch, P., Cohen, J., Tran, J., Catanzaro, B., Shelhamer, E.: cuDNN: E cient primitives for deep learning. arXiv preprint arXiv:1410.0759 (2014)

    3. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and other Kernel-based Learning Methods. Cambridge University Press (2000)

    4. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: A large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 248{255 (2009)

    5. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 770{778 (2016)

    6. Ibrahim, A., Abbott, A.L., Hussein, M.E.: An image dataset of text patches in everyday scenes. In: Proceedings of the International Symposium on Visual Computing (ISVC). pp. 291{300. Springer (2016)

    7. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Ca e: Convolutional architecture for fast feature embedding. In: 22nd ACM International Conference on Multimedia. pp. 675{678 (2014)

    8. Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Master's thesis, Department of Computer Science, University of Toronto (2009)

    9. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classi cation with deep convolutional neural networks. In: Proceedings of Advances in Neural Information Processing Systems (NIPS). pp. 1097{1105 (2012)

    10. LeCun, Y., Bottou, L., Bengio, Y., Ha ner, P.: Gradient-based learning applied to document recognition. In: Proceedings of the IEEE. vol. 86, pp. 2278{2324 (1998)

  • Related Research Results (2)
  • Metrics
    No metrics available
Share - Bookmark