Deep Neural Networks as a Computational Model for Human Shape Sensitivity.
about
Sensitivity to Nonaccidental Configurations of Two-Line Stimuli.Deep Convolutional Neural Networks Outperform Feature-Based But Not Categorical Models in Explaining Object Similarity Judgments.On the partnership between neural representations of object categories and visual features in the ventral visual pathway.Towards deep learning with segregated dendrites.Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision.Deep Residual Network Predicts Cortical Representation and Organization of Visual Features for Rapid Categorization.Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction.Foreground-Background Segmentation Revealed during Natural Image Viewing.Deep convolutional networks do not classify based on global object shape
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P2860
Deep Neural Networks as a Computational Model for Human Shape Sensitivity.
description
2016 nî lūn-bûn
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2016年の論文
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2016年学术文章
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2016年学术文章
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2016年学术文章
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2016年学术文章
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2016年学术文章
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2016年學術文章
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2016年學術文章
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2016年學術文章
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name
Deep Neural Networks as a Computational Model for Human Shape Sensitivity.
@ast
Deep Neural Networks as a Computational Model for Human Shape Sensitivity.
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type
label
Deep Neural Networks as a Computational Model for Human Shape Sensitivity.
@ast
Deep Neural Networks as a Computational Model for Human Shape Sensitivity.
@en
prefLabel
Deep Neural Networks as a Computational Model for Human Shape Sensitivity.
@ast
Deep Neural Networks as a Computational Model for Human Shape Sensitivity.
@en
P2860
P1476
Deep Neural Networks as a Computational Model for Human Shape Sensitivity.
@en
P2093
Hans P Op de Beeck
P2860
P304
P356
10.1371/JOURNAL.PCBI.1004896
P577
2016-04-28T00:00:00Z