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Link to original content: https://doi.org/10.1038/s42256-020-00265-z
Concept whitening for interpretable image recognition | Nature Machine Intelligence
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Concept whitening for interpretable image recognition

Nature Machine Intelligence volume 2, pages 772–782 (2020)Cite this article

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A preprint version of the article is available at arXiv.

Abstract

What does a neural network encode about a concept as we traverse through the layers? Interpretability in machine learning is undoubtedly important, but the calculations of neural networks are very challenging to understand. Attempts to see inside their hidden layers can be misleading, unusable or rely on the latent space to possess properties that it may not have. Here, rather than attempting to analyse a neural network post hoc, we introduce a mechanism, called concept whitening (CW), to alter a given layer of the network to allow us to better understand the computation leading up to that layer. When a concept whitening module is added to a convolutional neural network, the latent space is whitened (that is, decorrelated and normalized) and the axes of the latent space are aligned with known concepts of interest. By experiment, we show that CW can provide us with a much clearer understanding of how the network gradually learns concepts over layers. CW is an alternative to a batch normalization layer in that it normalizes, and also decorrelates (whitens), the latent space. CW can be used in any layer of the network without hurting predictive performance.

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Fig. 1: Possible data distributions in the latent space.
Fig. 2: Top-10 image activated on axes representing different concepts.
Fig. 3: Joint distribution of the bed–person subspace.
Fig. 4: Two-dimensional representation plot of two representative images.
Fig. 5: Normalized intra-concept and inter-concept similarities.
Fig. 6: Concept purity measured by AUC score.
Fig. 7: Concept importance to different Places365 classes measured on the concept axes when CW is applied to the 16th layer.

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Data availability

All datasets that support the findings are publicly available, including Places365 at http://places2.csail.mit.edu, MS COCO at https://cocodataset.org/ and ISIC at https://www.isic-archive.com.

Code availability

The code for replicating our experiments is available on https://github.com/zhiCHEN96/ConceptWhitening (https://doi.org/10.5281/zenodo.4052692).

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Acknowledgements

We are grateful to W. Zhang, L. Semenova, H. Parikh, C. Zhong, O. Li, C. Chen, and especially C. Tomasi and G. Sapiro for the feedback and assistance they provided during the development and preparation of this research. The authors acknowledge funding from MIT-Lincoln Laboratory and the National Science Foundation.

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Authors and Affiliations

  1. Department of Computer Science, Duke University, Durham, NC, USA

    Zhi Chen & Cynthia Rudin

  2. Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA

    Yijie Bei & Cynthia Rudin

Authors
  1. Zhi Chen
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  2. Yijie Bei
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  3. Cynthia Rudin
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Contributions

Z.C. and C.R. conceived the study. Z.C. developed methods, designed visualizations and metrics, ran experiments and contributed to the writing. Y.B. designed metrics, ran experiments and contributed to the writing. C.R. supervised research, method development and contributed to the writing.

Corresponding author

Correspondence to Zhi Chen.

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Peer review Information Nature Machine Intelligence thanks Professor Andreas Holzinger and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Extended data

Extended Data Fig. 1 Absolute correlation coefficient of every feature pair in the 16th layer.

a, when the 16th layer is a BN module; b, when 16th layer is a CW module.

Supplementary information

Supplementary Information

Supplementary sections and Figs. 1–13.

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Chen, Z., Bei, Y. & Rudin, C. Concept whitening for interpretable image recognition. Nat Mach Intell 2, 772–782 (2020). https://doi.org/10.1038/s42256-020-00265-z

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