Skip to main content
SpringerLink
Log in
Book cover

Human and Machine Learning pp 119–136Cite as

Critical Challenges for the Visual Representation of Deep Neural Networks

  • Chapter
  • First Online:

Part of the book series: Human–Computer Interaction Series ((HCIS))

Abstract

Artificial neural networks have proved successful in a broad range of applications over the last decade. However, there remain significant concerns about their interpretability. Visual representation is one way researchers are attempting to make sense of these models and their behaviour. The representation of neural networks raises questions which cross disciplinary boundaries. This chapter draws on a growing collection of interdisciplinary scholarship regarding neural networks. We present six case studies in the visual representation of neural networks and examine the particular representational challenges posed by these algorithms. Finally we summarise the ideas raised in the case studies as a set of takeaways for researchers engaging in this area.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Andrews, F.: CPPNX. https://floybix.github.io/cppnx/ (2017). Accessed 14 Dec 2017

  2. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv:1409.0473 (2014)

  3. Barocas, S., Selbst, A.: Big data’s disparate impact. Calif. Law Rev. 104(671), 671–732 (2016). https://doi.org/10.15779/Z38BG31

  4. Bastian, M., Heymann, S., Jacomy, M.: Others: Gephi: an open source software for exploring and manipulating networks. Icwsm 8, 361–362 (2009)

    Google Scholar 

  5. Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009)

    Article  Google Scholar 

  6. Benitez, J., Castro, J., Requena, I.: Are artificial neural networks black boxes? IEEE Trans. Neural Netw. 8(5), 1156–1164 (1997)

    Article  Google Scholar 

  7. Breiman, L.: Statistical modeling: the two cultures. Stat. Sci. 16(3), 199–231 (2001)

    Article  MathSciNet  Google Scholar 

  8. Browne, K.: Interpretable long-range LSTM cells visualisation redrawn from Karpathy Johnson and Fei-Fei 2015. https://gist.github.com/kieranbrowne/70d39b2d46a2444cb64e21f38b81c578 (2017). Accessed 11 Dec 2017

  9. Browne, K.: Neural interpretation diagram redrawn from özesmi and özesmi 2005. https://gist.github.com/kieranbrowne/a8d30f80484aebae796d62b85793dcc (2017). Accessed 11 Dec 2017

  10. Browne, K.: Neural interpretation diagram redrawn from TZENG and MA 2005. https://gist.github.com/kieranbrowne/8ca74d07adce15f39f0c59fe7bf76f17 (2017). Accessed 18 Dec 2017

  11. Browne, K.: Script for fooling images as in Nguyen Yosinski Clune (2015). https://gist.github.com/kieranbrowne/4f9fec38396e56cef88227c91283f242 (2017). Accessed 13 Dec 2017

  12. Burgess, M.: Gallery: ‘Brain scans’ map what happens during inside machine learning. http://www.wired.co.uk/gallery/machine-learning-graphcore-pictures-inside-ai. Accessed 13 Aug 2017

  13. Burrell, J.: How the machine thinks’: understanding opacity in machine learning algorithms. Big Data Soc. 3(1) (2016)

    Article  Google Scholar 

  14. Craven, M.W., Shavlik, J.W.: Visualizing learning and computation in artificial neural networks. Int. J. Artif. Intell. Tools 1(3), 399–425 (1992)

    Article  Google Scholar 

  15. Cresci, E.: FaceApp apologises for ‘racist’ filter that lightens users’ skintone. https://www.theguardian.com/technology/2017/apr/25/faceapp-apologises-for-racist-filter-which-lightens-users-skintone (2017). Accessed 30 Aug 2017

  16. Duch, W.: Coloring black boxes: visualization of neural network decisions. In: Proceedings of the International Joint Conference on Neural Networks, vol. 3, pp. 1735–1740. IEEE (2003)

    Google Scholar 

  17. Elgammal, A., Liu, B., Elhoseiny, M., Mazzone, M.: CAN: creative adversarial networks, generating “Art” by learning about styles and deviating from style norms. arXiv:1706.07068 (2017)

  18. FaceApp: Faceapp - free neural face transformation filters. https://www.faceapp.com/ (2017). Accessed 21 Dec 2017

  19. Fyles, M.: Inside an AI ’brain’ - What does machine learning look like? https://www.graphcore.ai/posts/what-does-machine-learning-look-like (2017). Accessed 13 Aug 2017

  20. Fyles, M.: Neural network structure, MSR ResNet-50 - large directed graph visualization [OC] : dataisbeautiful. https://www.reddit.com/r/dataisbeautiful/comments/5eowv6/neural_network_structure_msr_resnet50_large/. Accessed 13 Aug 2017

  21. Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)

    Article  Google Scholar 

  22. Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. arXiv:1611.07004 (2016)

  23. Karpathy, A., Johnson, J., Li, F.: Visualizing and understanding recurrent networks. CoRR arXiv:1506.02078 (2015)

  24. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25(NIPS2012), 1–9 (2012)

    Google Scholar 

  25. Längkvist, M., Karlsson, L., Loutfi, A.: A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognit. Lett. 42, 11–24 (2014)

    Article  Google Scholar 

  26. Le, Q.V.: Building high-level features using large scale unsupervised learning. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8595–8598. IEEE (2013)

    Google Scholar 

  27. Manovich, L.: The anti-sublime ideal in data art. http://meetopia.net/virus/pdf-ps_db/LManovich_data_art.pdf (2002). Accessed 21 Dec 2017

  28. Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., Riedmiller, M.: Playing atari with deep reinforcement learning. arXiv:1312.5602 (2013)

  29. Mordvintsev, A., Olah, C., Tyka, M.: Inceptionism: going deeper into neural networks. https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html. Accessed 12 Aug 2017

  30. Mordvintsev, A., Tyka, M., Olah, C.: Deep dreams (with Caffe). https://github.com/google/deepdream/blob/master/dream.ipynb (2017). Accessed 09 May 2017

  31. Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 427–436 (2015)

    Google Scholar 

  32. Olden, J.D., Jackson, D.A.: Illuminating the "black box": a randomization approach for understanding variable contributions in artificial neural networks. Ecol. Model. 154(1–2), 135–150 (2002)

    Article  Google Scholar 

  33. Özesmi, S.L., Özesmi, U.: An artificial neural network approach to spatial habitat modelling with interspecific interaction. Ecol. Model. 116(1), 15–31 (1999)

    Article  Google Scholar 

  34. Rosenblatt, F.: Principles of neurodynamics: perceptrons and the theory of brain mechanics. Spartan Book (1962)

    Google Scholar 

  35. Sainath, T.N., Vinyals, O., Senior, A., Sak, H.: Convolutional, long short-term memory, fully connected deep neural networks. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4580–4584. IEEE (2015)

    Google Scholar 

  36. Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. CoRR arXiv:1312.6034 (2013)

  37. Stanley, K.O.: Compositional pattern producing networks: a novel abstraction of development. Genet. Program. Evolvable Mach. 8(2), 131–162 (2007)

    Article  Google Scholar 

  38. Streeter, M., Ward, M., Alvarez, S.A.: NVIS: an interactive visualization tool for neural networks. In: Proceedings of Visual Data Exploration and Analysis Conference (2001)

    Google Scholar 

  39. Sussillo, D., Barak, O.: Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks. Neural Comput. 25(3), 626–49 (2013)

    Article  MathSciNet  Google Scholar 

  40. Tensorflow: Tensorboard: Graph visualization. https://www.tensorflow.org/get_started/graph_viz (2017). Accessed 21 Dec 2017

  41. Tunç, B.: Semantics of object representation in machine learning. Pattern Recognit. Lett. 64, 30–36 (2015). https://doi.org/10.1016/j.patrec.2015.03.016

    Article  Google Scholar 

  42. Tzeng, F.Y., Ma, K.L.: Opening the black box-data driven visualization of neural networks. Proceedings of IEEE Visualization 2005, 383–390 (2005)

    Google Scholar 

  43. Zahavy, T., Ben-Zrihem, N., Mannor, S.: Graying the black box: understanding DQNS. In: International Conference on Machine Learning, pp. 1899–1908 (2016)

    Google Scholar 

Download references

Acknowledgements

We are grateful for the helpful advice of Mitchell Whitelaw throughout the development of this chapter.

Author information

Authors and Affiliations

  1. Australian National University, Canberra, ACT, 0200, Australia

    Kieran Browne, Ben Swift & Henry Gardner

Authors
  1. Kieran Browne
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ben Swift
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Henry Gardner
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kieran Browne .

Editor information

Editors and Affiliations

  1. DATA61, CSIRO, Eveleigh, New South Wales, Australia

    Jianlong Zhou

  2. DATA61, CSIRO, Eveleigh, New South Wales, Australia

    Fang Chen

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Browne, K., Swift, B., Gardner, H. (2018). Critical Challenges for the Visual Representation of Deep Neural Networks. In: Zhou, J., Chen, F. (eds) Human and Machine Learning. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-319-90403-0_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-90403-0_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90402-3

  • Online ISBN: 978-3-319-90403-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation