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Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks
Rishi Rajalingham, View ORCID ProfileElias B. Issa, Pouya Bashivan, Kohitij Kar, Kailyn Schmidt, View ORCID ProfileJames J. DiCarlo
doi: https://doi.org/10.1101/240614
Rishi Rajalingham
McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Elias B. Issa
McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Pouya Bashivan
McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Kohitij Kar
McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Kailyn Schmidt
McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
James J. DiCarlo
McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
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Posted January 01, 2018.
Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks
Rishi Rajalingham, Elias B. Issa, Pouya Bashivan, Kohitij Kar, Kailyn Schmidt, James J. DiCarlo
bioRxiv 240614; doi: https://doi.org/10.1101/240614
Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks
Rishi Rajalingham, Elias B. Issa, Pouya Bashivan, Kohitij Kar, Kailyn Schmidt, James J. DiCarlo
bioRxiv 240614; doi: https://doi.org/10.1101/240614
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