Advertisement

Analysing the Limitations of Deep Learning for Developmental Robotics

  • Daniel CamilleriEmail author
  • Tony Prescott
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10384)

Abstract

Deep learning is a powerful approach to machine learning however its inherent disadvantages leave much to be desired in the pursuit of the perfect learning machine. This paper outlines the multiple disadvantages of deep learning and offers a view into the implications to solving these problems and how this would affect the state of the art not only in developmental learning but also in real world applications.

Keywords

Deep learning Artificial narrow intelligence Transfer learning Probabilistic models 

References

  1. 1.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  2. 2.
    Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Reognition, pp. 580–587 (2014)Google Scholar
  3. 3.
    Graves, A., Mohamed, A.r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (icassp), pp. 6645–6649. IEEE (2013)Google Scholar
  4. 4.
    van den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., Kavukcuoglu, K.: Wavenet: A generative model for raw audio. CoRR abs/1609.03499 (2016)Google Scholar
  5. 5.
    Arik, S.O., Chrzanowski, M., Coates, A., Diamos, G., Gibiansky, A., Kang, Y., Li, X., Miller, J., Raiman, J., Sengupta, S., et al.: Deep voice: Real-time neural text-to-speech. arXiv preprint (2017). arXiv:1702.07825
  6. 6.
    Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: The limitations of deep learning in adversarial settings. In: 2016 IEEE European Symposium on Security and Privacy (EuroS P), pp. 372–387 (2016)Google Scholar
  7. 7.
    Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 427–436 (2015)Google Scholar
  8. 8.
    Koopman, P., Wagner, M.: Challenges in autonomous vehicle testing and validation. SAE Int. J. Transp. Saf. 4(2016—-01–0128), 15–24 (2016)CrossRefGoogle Scholar
  9. 9.
    Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)CrossRefGoogle Scholar
  10. 10.
    McCloskey, M., Cohen, N.J.: Catastrophic interference in connectionist networks: The sequential learning problem. Psychol. Learn. Motiv. 24, 109–165 (1989)CrossRefGoogle Scholar
  11. 11.
    Goodfellow, I.J., Mirza, M., Xiao, D., Courville, A., Bengio, Y.: An empirical investigation of catastrophic forgetting in gradient-based neural networks. arXiv preprint (2013). arXiv:1312.6211
  12. 12.
    Fernando, C., Banarse, D., Blundell, C., Zwols, Y., Ha, D., Rusu, A.A., Pritzel, A., Wierstra, D.: Pathnet: Evolution channels gradient descent in super neural networks. arXiv preprint (2017). arXiv:1701.08734
  13. 13.
    Kahneman, D.: Thinking, Fast and Slow. Macmillan, New York (2011)Google Scholar
  14. 14.
    Wheeler, M.E., Petersen, S.E., Buckner, R.L.: Memory’s echo: Vivid remembering reactivates sensory-specific cortex. Proc. Nat. Acad. Sci. 97(20), 11125–11129 (2000)CrossRefGoogle Scholar
  15. 15.
    Wolfe, J.M.: Guided search 2.0 a revised model of visual search. Psychon. Bull. Rev. 1(2), 202–238 (1994)CrossRefGoogle Scholar
  16. 16.
    Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint (2015). arXiv:1511.06434

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Psychology DepartmentUniversity of SheffieldSheffieldUK

Personalised recommendations