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Robust deep learning object recognition models rely on low frequency information in natural images

Zhe Li, Josue Ortega Caro, Evgenia Rusak, Wieland Brendel, Matthias Bethge, Fabio Anselmi, Ankit B. Patel, Andreas S. Tolias, View ORCID ProfileXaq Pitkow
doi: https://doi.org/10.1101/2022.01.31.478509
Zhe Li
1Department of Neuroscience, Baylor College of Medicine, Houston, 77030, USA
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  • For correspondence: xaq@rice.edu
Josue Ortega Caro
1Department of Neuroscience, Baylor College of Medicine, Houston, 77030, USA
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Evgenia Rusak
2University of Tübingen, Germany
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Wieland Brendel
2University of Tübingen, Germany
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Matthias Bethge
2University of Tübingen, Germany
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Fabio Anselmi
1Department of Neuroscience, Baylor College of Medicine, Houston, 77030, USA
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Ankit B. Patel
1Department of Neuroscience, Baylor College of Medicine, Houston, 77030, USA
3Department of Electrical and Computer Engineering, Rice University, Houston, 77005, USA
4Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, 77030, USA
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Andreas S. Tolias
1Department of Neuroscience, Baylor College of Medicine, Houston, 77030, USA
3Department of Electrical and Computer Engineering, Rice University, Houston, 77005, USA
4Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, 77030, USA
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  • For correspondence: xaq@rice.edu
Xaq Pitkow
1Department of Neuroscience, Baylor College of Medicine, Houston, 77030, USA
3Department of Electrical and Computer Engineering, Rice University, Houston, 77005, USA
4Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, 77030, USA
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  • ORCID record for Xaq Pitkow
  • For correspondence: xaq@rice.edu
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ABSTRACT

Machine learning models have difficulty generalizing to data outside of the distribution they were trained on. In particular, vision models are usually vulnerable to adversarial attacks or common corruptions, to which the human visual system is robust. Recent studies have found that regularizing machine learning models to favor brain-like representations can improve model robustness, but it is unclear why. We hypothesize that the increased model robustness is partly due to the low spatial frequency preference inherited from the neural representation. We tested this simple hypothesis with several frequency-oriented analyses, including the design and use of hybrid images to probe model frequency sensitivity directly. We also examined many other publicly available robust models that were trained on adversarial images or with data augmentation, and found that all these robust models showed a greater preference to low spatial frequency information. We show that preprocessing by blurring can serve as a defense mechanism against both adversarial attacks and common corruptions, further confirming our hypothesis and demonstrating the utility of low spatial frequency information in robust object recognition.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵* co-first authors

  • ↵+ co-senior authors

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 02, 2022.
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Robust deep learning object recognition models rely on low frequency information in natural images
Zhe Li, Josue Ortega Caro, Evgenia Rusak, Wieland Brendel, Matthias Bethge, Fabio Anselmi, Ankit B. Patel, Andreas S. Tolias, Xaq Pitkow
bioRxiv 2022.01.31.478509; doi: https://doi.org/10.1101/2022.01.31.478509
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Robust deep learning object recognition models rely on low frequency information in natural images
Zhe Li, Josue Ortega Caro, Evgenia Rusak, Wieland Brendel, Matthias Bethge, Fabio Anselmi, Ankit B. Patel, Andreas S. Tolias, Xaq Pitkow
bioRxiv 2022.01.31.478509; doi: https://doi.org/10.1101/2022.01.31.478509

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