Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Non-exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Causal Explanation of Convolutional Neural Networks

Author : Hichem Debbi

Published in: Machine Learning and Knowledge Discovery in Databases. Research Track

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In this paper we introduce an explanation technique for Convolutional Neural Networks (CNNs) based on the theory of causality by Halpern and Pearl [12]. The causal explanation technique (CexCNN) is based on measuring the filter importance to a CNN decision, which is measured through counterfactual reasoning. In addition, we employ extended definitions of causality, which are responsibility and blame to weight the importance of such filters and project their contribution on input images. Since CNNs form a hierarchical structure, and since causal models can be hierarchically abstracted, we employ this similarity to perform the most important contribution of this paper, which is localizing the important features in the input image that contributed the most to a CNN’s decision. In addition to its ability in localization, we will show that CexCNN can be useful as well for model compression through pruning the less important filters. We tested CexCNN on several CNNs architectures and datasets. (The code is available on https://​github.​com/​HichemDebbi/​CexCNN)

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Off-Policy Differentiable Logic Reinforcement Learning
next chapter Interpretable Counterfactual Explanations Guided by Prototypes
Literature
1.
2.
3.
4.
Avanti, S., Peyton, G., Anshul, K.: Learning important features through propagating activation differences, pp. 3145–3153. ICML’17 (2017)
5.
Beckers, S., Halpern, J.Y.: Abstracting causal models. In: AAAI (2017)
6.
Chockler, H., Halpern, J.Y.: Responsibility and blame: a structural-model approach. J. Artif. Int. Res. 22(1), 93–115 (2004)MathSciNetMATH
7.
Choe, J., Oh, S.J., Lee, S., Chun, S., Akata, Z., Shim, H.: Evaluating weakly supervised object localization methods right. In: CVPR, pp. 3130–3139 (2020)
8.
Gordon, D., Kembhavi, A., Rastegari, M., Redmon, J., Fox, D., Farhadi, A.: Iqa: visual question answering in interactive environments. In: In arXiv:​1712.​03316 (2017)
9.
Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009)
10.
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR, pp. 580–587 (2014)
11.
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: ICLR (2015)
12.
Halpern, J., Pearl, J.: Causes and explanations: a structural-model approach part i: Causes. In: Proceedings of the 17th UAI, pp. 194–202 (2001)
13.
Halpern, J.Y., Pearl, J.: Causes and explanations: a structural-model approach. part ii: Explanations. Br. J. Philos. Sci. 56(4), 889–911 (2008)
14.
Han, S., Pool, J., Tran, J., Dally, W.J.: Learning both weights and connections for efficient neural networks. In: NIPS (2015)
15.
Harradon, M., Druce, J., Ruttenberg, B.E.: Causal learning and explanation of deep neural networks via autoencoded activations. In: CoRR abs/1802.00541 (2018)
16.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)CrossRef
17.
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRef
18.
19.
20.
Li, H., Kadav, A., Durdanovic, I., Samety, H.: Pruning filters for efficient convnets. In: ICLR 2017, pp. 1–13 (2017)
21.
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440 (2015)
22.
Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: NIPS, pp. 4768–4777 (2017)
23.
24.
Narendra, T., Sankaran, A., Vijaykeerthy, D., Mani, S.: Explaining deep learning models using causal inference. In: arXiv:​1811.​04376 (2018)
25.
Ribeiro, M.T., Singh, S., Guestrin, C.: “why should i trust you?": Explaining the predictions of any classifier, pp. 1135–1144. KDD ’16 (2016)
26.
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)MathSciNetCrossRef
27.
Schwab, P., Karlen, W.: Cxplain: causal explanations for model interpretation under uncertainty. NeurIPS, pp. 10220–10230 (2019)
28.
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: ICCV, pp. 618–626 (2017)
29.
Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. In: In arXiv:​1312.​6034 (2013)
30.
Singh, K.K., Lee, Y.J.: Forcing a network to be meticulous for weakly-supervised object and action localization. In: CVPR (2017)
31.
Smilkov, D., Thorat, N., Kim, B., Viegas, F.B., Wattenberg, M.: Smoothgrad: removing noise by adding noise. In: CoRR, vol. abs/1706.03825 (2017)
32.
Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: ICML, pp. 3319–3328 (2017)
33.
34.
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: CVPR, pp. 2921–2929 (2016)
Metadata
Title
Causal Explanation of Convolutional Neural Networks
Author
Hichem Debbi
Copyright Year
2021
Book
Machine Learning and Knowledge Discovery in Databases. Research Track

Print ISBN: 978-3-030-86519-1

Electronic ISBN: 978-3-030-86520-7

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-86520-7_39

Premium Partner

    Image Credits