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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
Josue Ortega Caro
1Department of Neuroscience, Baylor College of Medicine, Houston, 77030, USA
Evgenia Rusak
2University of Tübingen, Germany
Wieland Brendel
2University of Tübingen, Germany
Matthias Bethge
2University of Tübingen, Germany
Fabio Anselmi
1Department of Neuroscience, Baylor College of Medicine, Houston, 77030, USA
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
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
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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Posted February 02, 2022.
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
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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