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Discriminative Attribution from Paired Images

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Computer Vision – ECCV 2022 Workshops (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13804))

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Abstract

We present a method for deep neural network interpretability by combining feature attribution with counterfactual explanations to generate attribution maps that highlight the most discriminative features between classes. Crucially, this method can be used to quantitatively evaluate the performance of feature attribution methods in an objective manner, thus preventing potential observer bias. We evaluate the proposed method on six diverse datasets, and use it to discover so far unknown morphological features of synapses in Drosophila melanogaster. We show quantitatively and qualitatively that the highlighted features are substantially more discriminative than those extracted using conventional attribution methods and improve upon similar approaches for counterfactual explainability. We argue that the extracted explanations are better suited for understanding fine grained class differences as learned by a deep neural network, in particular for image domains where humans have little to no visual priors, such as biomedical datasets.

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Notes

  1. 1.

    Dataset kindly provided by the authors of [9].

References

  1. Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., Kim, B.: Sanity checks for saliency maps. arXiv preprint arXiv:1810.03292 (2018)

  2. Alvarez-Melis, D., Jaakkola, T.S.: On the robustness of interpretability methods. arXiv preprint arXiv:1806.08049 (2018)

  3. Ancona, M., Ceolini, E., Öztireli, C., Gross, M.: Towards better understanding of gradient-based attribution methods for deep neural networks. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=Sy21R9JAW

  4. 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), e0130140 (2015)

    Article  Google Scholar 

  5. Baehrens, D., Schroeter, T., Harmeling, S., Kawanabe, M., Hansen, K., Müller, K.R.: How to explain individual classification decisions. J. Mach. Learn. Res. 11, 1803–1831 (2010)

    MathSciNet  MATH  Google Scholar 

  6. Barredo-Arrieta, A., Del Ser, J.: Plausible counterfactuals: auditing deep learning classifiers with realistic adversarial examples. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–7. IEEE (2020)

    Google Scholar 

  7. Che, T., Li, Y., Jacob, A.P., Bengio, Y., Li, W.: Mode regularized generative adversarial networks. arXiv preprint arXiv:1612.02136 (2016)

  8. Dabkowski, P., Gal, Y.: Real time image saliency for black box classifiers. arXiv preprint arXiv:1705.07857 (2017)

  9. Eckstein, N., Bates, A.S., Du, M., Hartenstein, V., Jefferis, G.S., Funke, J.: Neurotransmitter classification from electron microscopy images at synaptic sites in drosophila. BioRxiv (2020)

    Google Scholar 

  10. Fong, R.C., Vedaldi, A.: Interpretable explanations of black boxes by meaningful perturbation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3429–3437 (2017)

    Google Scholar 

  11. Ghorbani, A., Abid, A., Zou, J.: Interpretation of neural networks is fragile. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 3681–3688 (2019)

    Google Scholar 

  12. Goyal, Y., Wu, Z., Ernst, J., Batra, D., Parikh, D., Lee, S.: Counterfactual visual explanations. In: International Conference on Machine Learning, pp. 2376–2384. PMLR (2019)

    Google Scholar 

  13. 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, pp. 770–778 (2016)

    Google Scholar 

  14. Hooker, S., Erhan, D., Kindermans, P.J., Kim, B.: A benchmark for interpretability methods in deep neural networks. arXiv preprint arXiv:1806.10758 (2018)

  15. Kindermans, P.-J., et al.: The (Un)reliability of saliency methods. In: Samek, W., Montavon, G., Vedaldi, A., Hansen, L.K., Müller, K.-R. (eds.) Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. LNCS (LNAI), vol. 11700, pp. 267–280. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-28954-6_14

    Chapter  Google Scholar 

  16. Kindermans, P.J., Schütt, K.T., Alber, M., Müller, K.R., Erhan, D., Kim, B., Dähne, S.: Learning how to explain neural networks: Patternnet and patternattribution. arXiv preprint arXiv:1705.05598 (2017)

  17. LeCun, Y., Cortes, C.: MNIST handwritten digit database (2010). http://yann.lecun.com/exdb/mnist/

  18. Liu, S., Kailkhura, B., Loveland, D., Han, Y.: Generative counterfactual introspection for explainable deep learning. arXiv preprint arXiv:1907.03077 (2019)

  19. Lundberg, S., Lee, S.I.: A unified approach to interpreting model predictions. arXiv preprint arXiv:1705.07874 (2017)

  20. Martens, D., Provost, F.: Explaining data-driven document classifications. MIS Q. 38(1), 73–100 (2014)

    Article  Google Scholar 

  21. Montavon, G., Lapuschkin, S., Binder, A., Samek, W., Müller, K.R.: Explaining nonlinear classification decisions with deep Taylor decomposition. Pattern Recogn. 65, 211–222 (2017)

    Article  Google Scholar 

  22. Mothilal, R.K., Sharma, A., Tan, C.: Explaining machine learning classifiers through diverse counterfactual explanations. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 607–617 (2020)

    Google Scholar 

  23. Narayanaswamy, A., et al.: Scientific discovery by generating counterfactuals using image translation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 273–283. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59710-8_27

    Chapter  Google Scholar 

  24. Ribeiro, M.T., Singh, S., Guestrin, C.: Model-agnostic interpretability of machine learning. arXiv preprint arXiv:1606.05386 (2016)

  25. Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. arXiv preprint arXiv:1606.03498 (2016)

  26. Samek, W., Binder, A., Montavon, G., Lapuschkin, S., Müller, K.R.: Evaluating the visualization of what a deep neural network has learned. IEEE Trans. Neural Netw. Learn. Syst. 28(11), 2660–2673 (2016)

    Article  MathSciNet  Google Scholar 

  27. Samek, W., Montavon, G., Lapuschkin, S., Anders, C.J., Müller, K.R.: Explaining deep neural networks and beyond: a review of methods and applications. Proc. IEEE 109(3), 247–278 (2021)

    Article  Google Scholar 

  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: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)

    Google Scholar 

  29. Shrikumar, A., Greenside, P., Kundaje, A.: Learning important features through propagating activation differences. In: International Conference on Machine Learning, pp. 3145–3153. PMLR (2017)

    Google Scholar 

  30. Shrikumar, A., Greenside, P., Shcherbina, A., Kundaje, A.: Not just a black box: learning important features through propagating activation differences. arXiv preprint arXiv:1605.01713 (2016)

  31. Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps (2014)

    Google Scholar 

  32. Smilkov, D., Thorat, N., Kim, B., Viégas, F., Wattenberg, M.: SmoothGrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825 (2017)

  33. Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: the all convolutional net. arXiv preprint arXiv:1412.6806 (2014)

  34. Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: Proceedings of the 34th International Conference on Machine Learning, Vol. 70, pp. 3319–3328. JMLR.org (2017)

    Google Scholar 

  35. Verma, S., Dickerson, J., Hines, K.: Counterfactual explanations for machine learning: a review. arXiv preprint arXiv:2010.10596 (2020)

  36. Wachter, S., Mittelstadt, B., Russell, C.: Counterfactual explanations without opening the black box: automated decisions and the GDPR. Harv. JL Tech. 31, 841 (2017)

    Google Scholar 

  37. Wang, P., Vasconcelos, N.: Scout: self-aware discriminant counterfactual explanations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8981–8990 (2020)

    Google Scholar 

  38. Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53

    Chapter  Google Scholar 

  39. Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: Understanding deep learning requires rethinking generalization. arXiv preprint arXiv:1611.03530 (2016)

  40. Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)

    Google Scholar 

  41. Zintgraf, L.M., Cohen, T.S., Adel, T., Welling, M.: Visualizing deep neural network decisions: prediction difference analysis. arXiv preprint arXiv:1702.04595 (2017)

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Author information

Authors and Affiliations

  1. HHMI Janelia Research Campus, Ashburn, USA

    Nils Eckstein, Habib Bukhari & Jan Funke

  2. Harvard Medical School, Boston, MA, USA

    Alexander S. Bates

  3. MRC Laboratory of Molecular Biology, Cambridge, CB2 0QH, UK

    Gregory S. X. E. Jefferis

Authors
  1. Nils Eckstein
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  2. Habib Bukhari
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  3. Alexander S. Bates
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  4. Gregory S. X. E. Jefferis
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  5. Jan Funke
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Corresponding author

Correspondence to Nils Eckstein .

Editor information

Editors and Affiliations

  1. IBM Research - MIT-IBM Watson AI Lab, Massachusetts, USA

    Leonid Karlinsky

  2. Technion – Israel Institute of Technology, Haifa, Israel

    Tomer Michaeli

  3. Kyoto University, Kyoto, Japan

    Ko Nishino

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Eckstein, N., Bukhari, H., Bates, A.S., Jefferis, G.S.X.E., Funke, J. (2023). Discriminative Attribution from Paired Images. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13804. Springer, Cham. https://doi.org/10.1007/978-3-031-25069-9_27

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  • DOI: https://doi.org/10.1007/978-3-031-25069-9_27

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