ArticleJournal of vision2021
Convolutional neural networks trained with a developmental sequence of blurry to clear images reveal core differences between face and object processing.
Article in Journal of vision, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Systematic image perturbations reveal persistent gaps between human and machine vision.iScience · 2026Article
- The state of modelling face processing in humans with deep learning.British journal of psychology (London, England : 1953) · 2026Review
- Exposure to naturalistic occlusion promotes generalized, human-like robustness in deep neural networks.bioRxiv : the preprint server for biology · 2026Article
- Computationally Probing the Role of Time-Limited Neuronal Plasticity in Early Visual Development.Developmental science · 2026Article
- The blur paradox: Better recognition at a distance.Journal of vision · 2025Article
- Evaluating the robustness of slide-level AI predictions on out-of-focus whole slide images: A retrospective observational study.Journal of pathology informatics · 2025Article
- Category-specific perceptual learning of robust object recognition modelled using deep neural networks.PLoS computational biology · 2025Article
- Potential role of developmental experience in the emergence of the parvo-magno distinction.Communications biology · 2025Article
- Configural processing as an optimized strategy for robust object recognition in neural networks.Communications biology · 2025Article
- Development of Higher-Level Vision: A Network Perspective.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Review
- Improved modeling of human vision by incorporating robustness to blur in convolutional neural networks.Nature communications · 2024Article
- Butterfly effects in perceptual development: a review of the 'adaptive initial degradation' hypothesis.Developmental review : DR · 2024Article
- A novel feature-scrambling approach reveals the capacity of convolutional neural networks to learn spatial relations.Neural networks : the official journal of the International Neural Network Society · 2023Article
- Early experience with low-pass filtered images facilitates visual category learning in a neural network model.PloS one · 2023Article
- Does training with blurred images bring convolutional neural networks closer to humans with respect to robust object recognition and internal representations?Frontiers in psychology · 2023Article
- Perception of an object's global shape is best described by a model of skeletal structure in human infants.eLife · 2022Article
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2 authors.
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Abstract
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, given that acuity is initially poor but improves considerably over the first several months of life? Here, we evaluated the potential consequences of such early experiences by training CNN models on face and object recognition tasks while gradually reducing the amount of blur applied to the training images. For CNNs trained on blurry to clear faces, we observed sustained robustness to blur, consistent with a recent report by Vogelsang and colleagues (2018). By contrast, CNNs trained with blurry to clear objects failed to retain robustness to blur. Further analyses revealed that the spatial frequency tuning of the two CNNs was profoundly different. The blurry to clear face-trained network successfully retained a preference for low spatial frequencies, whereas the blurry to clear object-trained CNN exhibited a progressive shift toward higher spatial frequencies. Our findings provide novel computational evidence showing how face recognition, unlike object recognition, allows for more holistic processing. Moreover, our results suggest that blurry vision during infancy is insufficient to account for the robustness of adult vision to blurry objects.
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