An ecologically motivated image dataset for deep learning yields better models of human vision
出版年份 2021 全文链接
标题
An ecologically motivated image dataset for deep learning yields better models of human vision
作者
关键词
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出版物
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 118, Issue 8, Pages e2011417118
出版商
Proceedings of the National Academy of Sciences
发表日期
2021-02-17
DOI
10.1073/pnas.2011417118
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- A map of object space in primate inferotemporal cortex
- (2020) Pinglei Bao et al. NATURE
- Recurrent neural networks can explain flexible trading of speed and accuracy in biological vision
- (2020) Courtney J. Spoerer et al. PLoS Computational Biology
- Revealing the multidimensional mental representations of natural objects underlying human similarity judgements
- (2020) Martin N. Hebart et al. Nature Human Behaviour
- Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior
- (2019) Kohitij Kar et al. NATURE NEUROSCIENCE
- Deep Neural Networks as Scientific Models
- (2019) Radoslaw M. Cichy et al. TRENDS IN COGNITIVE SCIENCES
- Peeling the Onion of Brain Representations
- (2019) Nikolaus Kriegeskorte et al. Annual Review of Neuroscience
- A deep learning framework for neuroscience
- (2019) Blake A. Richards et al. NATURE NEUROSCIENCE
- THINGS: A database of 1,854 object concepts and more than 26,000 naturalistic object images
- (2019) Martin N. Hebart et al. PLoS One
- Recurrence is required to capture the representational dynamics of the human visual system
- (2019) Tim C. Kietzmann et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks
- (2018) Rishi Rajalingham et al. JOURNAL OF NEUROSCIENCE
- Generic decoding of seen and imagined objects using hierarchical visual features
- (2017) Tomoyasu Horikawa et al. Nature Communications
- The Functional Neuroanatomy of Human Face Perception
- (2017) Kalanit Grill-Spector et al. Annual Review of Vision Science
- A multi-modal parcellation of human cerebral cortex
- (2016) Matthew F. Glasser et al. NATURE
- ImageNet Large Scale Visual Recognition Challenge
- (2015) Olga Russakovsky et al. INTERNATIONAL JOURNAL OF COMPUTER VISION
- Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream
- (2015) U. Guclu et al. JOURNAL OF NEUROSCIENCE
- Computational neuroimaging and population receptive fields
- (2015) Brian A. Wandell et al. TRENDS IN COGNITIVE SCIENCES
- Resolving human object recognition in space and time
- (2014) Radoslaw Martin Cichy et al. NATURE NEUROSCIENCE
- Performance-optimized hierarchical models predict neural responses in higher visual cortex
- (2014) D. L. K. Yamins et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation
- (2014) Seyed-Mahdi Khaligh-Razavi et al. PLoS Computational Biology
- Tripartite Organization of the Ventral Stream by Animacy and Object Size
- (2013) T. Konkle et al. JOURNAL OF NEUROSCIENCE
- Coherent concepts are computed in the anterior temporal lobes
- (2010) M. A. Lambon Ralph et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- An anterior temporal face patch in human cortex, predicted by macaque maps
- (2009) Reza Rajimehr et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey
- (2008) Nikolaus Kriegeskorte et al. NEURON
- Comparing face patch systems in macaques and humans
- (2008) D. Y. Tsao et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- How does nature program neuron types?
- (2008) Alexander Borst Frontiers in Neuroscience
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