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Convolutional neural networks trained with a developmental sequence of blurry to clear images reveal core differences between face and object processing
Although convolutional neural networks (CNNs) provide a promising model for understanding human vision, most CNNs lack robustness to challenging viewing conditions, such as image blur, whereas human vision is much more reliable. Might robustness to blur be attributable to vision during infancy, give...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
The Association for Research in Vision and Ophthalmology
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8590164/ https://www.ncbi.nlm.nih.gov/pubmed/34767621 http://dx.doi.org/10.1167/jov.21.12.6 |