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The developmental trajectory of object recognition robustness: Children are like small adults but unlike big deep neural networks

  • Published:2023-07-06 Issue:7 Volume:23 Page:4
  • ISSN:1534-7362
  • Container-title:Journal of Vision
  • language:en
  • Short-container-title:Journal of Vision

Author:

Huber Lukas S.1234,Geirhos Robert256,Wichmann Felix A.278

Affiliation:

1. Department of Psychology, University of Bern, Bern, Switzerland

2. Neural Information Processing Group, University of Tübingen, Tübingen, Germany

3. https://orcid.org/0000-0002-7755-6926

4. lukas.s.huber@unibe.ch

5. https://orcid.org/0000-0001-7698-3187

6. robert.geirhos@uni-tuebingen.de

7. https://orcid.org/0000-0002-2592-634X

8. felix.wichmann@uni-tuebingen.de

Publisher

Association for Research in Vision and Ophthalmology (ARVO)

Subject

Sensory Systems,Ophthalmology

Reference96 articles.

1. Parts and relations in young children's shape-based object recognition;Augustine;Journal of Cognition and Development,,2011

2. Development of object recognition;Ayzenberg;PsyArXiv,2022

3. Young children outperform feed-forward and recurrent neural networks on challenging object recognition tasks;Ayzenberg;Journal of Vision,,2020

4. Deep convolutional networks do not classify based on global object shape;Baker;PLoS Computational Biology,,2018

5. Toddler-inspired visual object learning;Bambach;32nd Conference on Neural Information Processing Systems (NeurIPS),2018

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