Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition
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Cadieu-2014-Deep Neural Networks.pdf
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Author(s)
Cadieu, Charles
Hong, Ha
Yamins, Daniel L. K.
Pinto, Nicolas
Ardila, Diego
Solomon, Ethan A.
Majaj, Najib J.
DiCarlo, James
Date Issued
December 2014
Journal
PLoS Computational Biology
Publisher
Public Library of Science
Citation
Cadieu, Charles F., Ha Hong, Daniel L. K. Yamins, Nicolas Pinto, Diego Ardila, Ethan A. Solomon, Najib J. Majaj, and James J. DiCarlo. “Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition.” Edited by Matthias Bethge. PLoS Comput Biol 10, no. 12 (December 18, 2014): e1003963.
Version
Final published version
Abstract
The primate visual system achieves remarkable visual object recognition performance even in brief presentations, and under changes to object exemplar, geometric transformations, and background variation (a.k.a. core visual object recognition). This remarkable performance is mediated by the representation formed in inferior temporal (IT) cortex. In parallel, recent advances in machine learning have led to ever higher performing models of object recognition using artificial deep neural networks (DNNs). It remains unclear, however, whether the representational performance of DNNs rivals that of the brain. To accurately produce such a comparison, a major difficulty has been a unifying metric that accounts for experimental limitations, such as the amount of noise, the number of neural recording sites, and the number of trials, and computational limitations, such as the complexity of the decoding classifier and the number of classifier training examples. In this work, we perform a direct comparison that corrects for these experimental limitations and computational considerations. As part of our methodology, we propose an extension of “kernel analysis” that measures the generalization accuracy as a function of representational complexity. Our evaluations show that, unlike previous bio-inspired models, the latest DNNs rival the representational performance of IT cortex on this visual object recognition task. Furthermore, we show that models that perform well on measures of representational performance also perform well on measures of representational similarity to IT, and on measures of predicting individual IT multi-unit responses. Whether these DNNs rely on computational mechanisms similar to the primate visual system is yet to be determined, but, unlike all previous bio-inspired models, that possibility cannot be ruled out merely on representational performance grounds.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
McGovern Institute for Brain Research at MIT
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DOI of Published Version
https://doi.org/10.1371/journal.pcbi.1003963
