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Improved modeling of human vision by incorporating robustness to blur in convolutional neural networks

Author

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  • Hojin Jang

    (Vanderbilt Vision Research Center, Vanderbilt University
    Massachusetts Institute of Technology
    Korea University)

  • Frank Tong

    (Vanderbilt Vision Research Center, Vanderbilt University)

Abstract

Whenever a visual scene is cast onto the retina, much of it will appear degraded due to poor resolution in the periphery; moreover, optical defocus can cause blur in central vision. However, the pervasiveness of blurry or degraded input is typically overlooked in the training of convolutional neural networks (CNNs). We hypothesized that the absence of blurry training inputs may cause CNNs to rely excessively on high spatial frequency information for object recognition, thereby causing systematic deviations from biological vision. We evaluated this hypothesis by comparing standard CNNs with CNNs trained on a combination of clear and blurry images. We show that blur-trained CNNs outperform standard CNNs at predicting neural responses to objects across a variety of viewing conditions. Moreover, blur-trained CNNs acquire increased sensitivity to shape information and greater robustness to multiple forms of visual noise, leading to improved correspondence with human perception. Our results provide multi-faceted neurocomputational evidence that blurry visual experiences may be critical for conferring robustness to biological visual systems.

Suggested Citation

  • Hojin Jang & Frank Tong, 2024. "Improved modeling of human vision by incorporating robustness to blur in convolutional neural networks," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45679-0
    DOI: 10.1038/s41467-024-45679-0
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    References listed on IDEAS

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    1. Timothy D. Oleskiw & Amy Nowack & Anitha Pasupathy, 2018. "Joint coding of shape and blur in area V4," Nature Communications, Nature, vol. 9(1), pages 1-13, December.
    2. Tomoyasu Horikawa & Yukiyasu Kamitani, 2017. "Generic decoding of seen and imagined objects using hierarchical visual features," Nature Communications, Nature, vol. 8(1), pages 1-15, August.
    3. J. Gold & P. J. Bennett & A. B. Sekuler, 1999. "Signal but not noise changes with perceptual learning," Nature, Nature, vol. 402(6758), pages 176-178, November.
    4. Pinglei Bao & Liang She & Mason McGill & Doris Y. Tsao, 2020. "A map of object space in primate inferotemporal cortex," Nature, Nature, vol. 583(7814), pages 103-108, July.
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