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Improving transparency of deep neural inference process

  • Hiroshi KuwajimaEmail author
  • Masayuki Tanaka
  • Masatoshi Okutomi
Regular Paper

Abstract

Deep learning techniques are rapidly advanced recently and becoming a necessity component for widespread systems. However, the inference process of deep learning is black box and is not very suitable to safety-critical systems which must exhibit high transparency. In this paper, to address this black-box limitation, we develop a simple analysis method which consists of (1) structural feature analysis: lists of the features contributing to inference process, (2) linguistic feature analysis: lists of the natural language labels describing the visual attributes for each feature contributing to inference process, and (3) consistency analysis: measuring consistency among input data, inference (label), and the result of our structural and linguistic feature analysis. Our analysis is simplified to reflect the actual inference process for high transparency, whereas it does not include any additional black-box mechanisms such as LSTM for highly human readable results. We conduct experiments and discuss the results of our analysis qualitatively and quantitatively and come to believe that our work improves the transparency of neural networks. Evaluated through 12,800 human tasks, 75% workers answer that input data and result of our feature analysis are consistent, and 70% workers answer that inference (label) and result of our feature analysis are consistent. In addition to the evaluation of the proposed analysis, we find that our analysis also provides suggestions, or possible next actions such as expanding neural network complexity or collecting training data to improve a neural network.

Keywords

Transparency Deep neural network Black box Explainable AI Visualization Visual attribute 

Notes

References

  1. 1.
    Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10(7), e0130,140 (2015).  https://doi.org/10.1371/journal.pone.0130140 CrossRefGoogle Scholar
  2. 2.
    Bau, D., Zhou, B., Khosla, A., Oliva, A., Torralba, A.: Network dissection: quantifying interpretability of deep visual representations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)Google Scholar
  3. 3.
    Binder, A., Montavon, G., Lapuschkin, S., Müller, K., Samek, W.: Layer-wise relevance propagation for neural networks with local renormalization layers. In: Villa, A. E. P., Masulli, P., Rivero, A. J. P. (eds.) Artificial Neural Networks and Machine Learning—ICANN 2016—25th International Conference on Artificial Neural Networks, Barcelona, Spain, 6–9 Sept, 2016, Proceedings, Part II, Springer, Lecture Notes in Computer Science, vol 9887, pp. 63–71 (2016)  https://doi.org/10.1007/978-3-319-44781-0_8
  4. 4.
    Bojarski, M., Testa, D.D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L.D., Monfort, M., Muller, U., Zhang, J., Zhang, X., Zhao, J., Zieba, K.: End to end learning for self-driving cars. CoRR (2016) arXiv:1604.07316
  5. 5.
    Bojarski, M., Choromanska, A., Choromanski, K., Firner, B., Ackel, L.J., Muller, U., Yeres, P., Zieba, K.: Visualbackprop: Efficient visualization of cnns for autonomous driving. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1–8 (2018)Google Scholar
  6. 6.
    Choo, J., Liu, S.: Visual analytics for explainable deep learning. IEEE Comput. Graph. Appl. 38(4), 84–92 (2018).  https://doi.org/10.1109/MCG.2018.042731661 CrossRefGoogle Scholar
  7. 7.
    Dam, H.K., Tran, T., Ghose, A.: Explainable software analytics. In: Zisman, A., Apel, S. (eds.) Proceedings of the 40th International Conference on Software Engineering: New Ideas and Emerging Results, ICSE (NIER) 2018, Gothenburg, Sweden, May 27–June 03, 2018, pp. 53–56. ACM (2018).  https://doi.org/10.1145/3183399.3183424
  8. 8.
    Ding, W., Wang, R., Mao, F., Taylor, G.: Theano-based large-scale visual recognition with multiple gpus (2014) arXiv preprint arXiv:1412.2302
  9. 9.
    Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: a survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018)Google Scholar
  10. 10.
    Escorcia, V., Niebles, J.C., Ghanem, B.: On the relationship between visual attributes and convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, 7–12 June, 2015, pp. 1256–1264 (2015).  https://doi.org/10.1109/CVPR.2015.7298730
  11. 11.
    Fukushima, K., Miyake, S.: Neocognitron: a new algorithm for pattern recognition tolerant of deformations and shifts in position. Pattern Recognit. 15(6), 455–469 (1982).  https://doi.org/10.1016/0031-3203(82)90024-3 CrossRefGoogle Scholar
  12. 12.
    Ganesan, K.: Computing precision and recall for multi-class classification problems. http://text-analytics101.rxnlp.com/2014/10/computing-precision-and-recall-for.html (2014)
  13. 13.
    Graves, A., Jaitly, N., Mohamed, A.: Hybrid speech recognition with deep bidirectional LSTM. In: 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, Olomouc, Czech Republic, 8–12 Dec, 2013, pp. 273–278. IEEE (2013)  https://doi.org/10.1109/ASRU.2013.6707742
  14. 14.
    Grün, F., Rupprecht, C., Navab, N., Federico, T.: A taxonomy and library for visualizing learned features in convolutional neural networks. In: ICML Workshop on Visualization for Deep Learning (ICML-W) (2016)Google Scholar
  15. 15.
    Gunning, D.: (2016) Explainable artificial intelligence (xai). https://www.darpa.mil/program/explainable-artificial-intelligence
  16. 16.
    He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), IEEE Computer Society, Washington, DC, USA, ICCV ’15, pp. 1026–1034 (2015).  https://doi.org/10.1109/ICCV.2015.123
  17. 17.
    Hendricks, L.A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., Darrell, T.: Generating visual explanations. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision—ECCV 2016—14th European Conference, Amsterdam, The Netherlands, 11–14 Oct, 2016, Proceedings, Part IV, Springer, Lecture Notes in Computer Science, vol. 9908, pp. 3–19 (2016).  https://doi.org/10.1007/978-3-319-46493-0_1
  18. 18.
    Hohman, F.M., Kahng, M., Pienta, R., Chau, D.H.: Visual analytics in deep learning: An interrogative survey for the next frontiers. In: IEEE Transactions on Visualization & Computer Graphics (2018).  https://doi.org/10.1109/TVCG.2018.2843369
  19. 19.
    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
  20. 20.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105. Curran Associates, Inc. (2012)Google Scholar
  21. 21.
    Kuwajima, H., Tanaka, M.: Network analysis for explanation. In: Transparent and interpretable Machine Learning in Safety Critical Environments (NIPS2017 Workshop) (2017)Google Scholar
  22. 22.
    Kuwajima, H., Yasuoka, H., Nakae, T.: Open problems in engineering and quality assurance of safety critical machine learning systems. In: Joint Workshop Between ICML, AAMAS and IJCAI on Deep (or Machine) Learning for Safety-Critical Applications in Engineering (2018)Google Scholar
  23. 23.
    Lin, M., Chen, Q., Yan, S.: Network in network. CoRR (2013). arXiv:1312.4400
  24. 24.
    Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds) Advances in Neural Information Processing Systems, vol. 30, pp. 4765–4774. Curran Associates, Inc. (2017)Google Scholar
  25. 25.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, GS., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Burges, C.J.C, Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 26, pp. 3111–3119. Curran Associates, Inc. (2013)Google Scholar
  26. 26.
    Miller, G.A., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.J.: Introduction to WordNet: an on-line lexical database. Int. J. Lexicogr. 3(4), 235–244 (1990)CrossRefGoogle Scholar
  27. 27.
    Montavon, G., Lapuschkin, S., Binder, A., Samek, W., Müller, K.: Explaining nonlinear classification decisions with deep taylor decomposition. Pattern Recognit. 65, 211–222 (2017).  https://doi.org/10.1016/j.patcog.2016.11.008 CrossRefGoogle Scholar
  28. 28.
    Montavon, G., Samek, W., Müller, K.: Methods for interpreting and understanding deep neural networks. Digital Signal Process. 73, 1–15 (2018)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Park, D.H., Hendricks, L.A., Akata, Z., Schiele, B., Darrell, T., Rohrbach, M.: Attentive explanations: justifying decisions and pointing to the evidence. CoRR (2016). arXiv:1612.04757
  30. 30.
    Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)Google Scholar
  31. 31.
    Powers, D.M.W.: Evaluation: From precision, recall and f-measure to roc., informedness, markedness & correlation. J. Mach. Learn. Technol. 2(1), 37–63 (2011)MathSciNetGoogle Scholar
  32. 32.
    Ribeiro, M.T., Singh, S., Guestrin, C.: ”why should I trust you?”: Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 Aug, 2016, pp. 1135–1144 (2016)Google Scholar
  33. 33.
    Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115(3), 211–252 (2015).  https://doi.org/10.1007/s11263-015-0816-y MathSciNetCrossRefGoogle Scholar
  34. 34.
    Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: The IEEE International Conference on Computer Vision (ICCV) (2017)Google Scholar
  35. 35.
    Shrikumar, A., Greenside, P., Shcherbina, A., Kundaje, A.: Not just a black box: learning important features through propagating activation differences. CoRR (2013). arXiv:1605.01713
  36. 36.
    Shrikumar, A., Greenside, P., Kundaje, A.: Learning important features through propagating activation differences. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning, PMLR, International Convention Centre, Sydney, Australia, Proceedings of Machine Learning Research, vol. 70, pp. 3145–3153 (2017)Google Scholar
  37. 37.
    Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. In: Proceedings of the International Conference on Learning Representations (ICLR) (2014)Google Scholar
  38. 38.
    Uchida, K., Tanaka, M., Okutomi, M.: Coupled convolution layer for convolutional neural network. Neural Netw. 105, 197–205 (2018)CrossRefGoogle Scholar
  39. 39.
    Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: Show and tell: A neural image caption generator. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, 7–12 June, 2015, pp. 3156–3164 (2015).  https://doi.org/10.1109/CVPR.2015.7298935
  40. 40.
    Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A.C., Salakhutdinov, R., Zemel, R.S., Bengio, Y.: Show, attend and tell: neural image caption generation with visual attention. In: Bach, F.R., Blei, D.M. (eds) Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6–11 July 2015, JMLR.org, JMLR Workshop and Conference Proceedings, vol. 37, pp. 2048–2057 (2015)Google Scholar
  41. 41.
    Young, T., Hazarika, D., Poria, S., Cambria, E.: Recent trends in deep learning based natural language processing. IEEE Comput. Intell. Mag. 13, 55–75 (2018).  https://doi.org/10.1109/MCI.2018.2840738 CrossRefGoogle Scholar
  42. 42.
    Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) Computer Vision—ECCV 2014, pp. 818–833. Springer International Publishing, Cham (2014)Google Scholar
  43. 43.
    Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Systems and Control EngineeringTokyo Institute of TechnologyTokyoJapan
  2. 2.Technology Planning DivisionDENSO CORPORATIONKariya, AichiJapan
  3. 3.Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and TechnologyTokyoJapan

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