Deep neural networks capture texture sensitivity in V2

Md Nasir Uddin Laskar; Luis Gonzalo Sanchez Giraldo; Odelia Schwartz

doi:10.1167/JOV.20.7.21

Deep neural networks capture texture sensitivity in V2

Md Nasir Uddin Laskar, Luis Gonzalo Sanchez Giraldo, Odelia Schwartz

Research output: Contribution to journal › Article › peer-review

15 Scopus citations

Original language	English
Article number	21
Journal	Journal of Vision
Volume	20
Issue number	7
DOIs	https://doi.org/10.1167/JOV.20.7.21
State	Published - Jul 2020

Bibliographical note

Funding Information:
The authors thank Adam Kohn, Ruben Coen Cagli, and Corey Ziemba for discussions and very helpful comments on an earlier version of the manuscript; Eero Simoncelli and Corey Ziemba for discussions and providing us with the original texture data set used in the experiments; and David Grossman and Ariel Lavi for discussions during their REU research. This work was partly supported by a Google Faculty Research Award, the National Science Foundation (grant 1715475), and the National Institutes of Health (grant 1R01EY024858-01A1).

Funding

The authors thank Adam Kohn, Ruben Coen Cagli, and Corey Ziemba for discussions and very helpful comments on an earlier version of the manuscript; Eero Simoncelli and Corey Ziemba for discussions and providing us with the original texture data set used in the experiments; and David Grossman and Ariel Lavi for discussions during their REU research. This work was partly supported by a Google Faculty Research Award, the National Science Foundation (grant 1715475), and the National Institutes of Health (grant 1R01EY024858-01A1).

Funders	Funder number
National Science Foundation Arctic Social Science Program	1715475
National Institutes of Health (NIH)
National Eye Institute (NEI)	R01EY024858
Google

Keywords

Deep learning
Deep neural network
Mid-level vision
Texture perception
Visual area V2
Visual cortex

ASJC Scopus subject areas

Ophthalmology
Sensory Systems

Access to Document

10.1167/JOV.20.7.21

Cite this

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title = "Deep neural networks capture texture sensitivity in V2",

keywords = "Deep learning, Deep neural network, Mid-level vision, Texture perception, Visual area V2, Visual cortex",

author = "Laskar, \{Md Nasir Uddin\} and Giraldo, \{Luis Gonzalo Sanchez\} and Odelia Schwartz",

note = "Funding Information: The authors thank Adam Kohn, Ruben Coen Cagli, and Corey Ziemba for discussions and very helpful comments on an earlier version of the manuscript; Eero Simoncelli and Corey Ziemba for discussions and providing us with the original texture data set used in the experiments; and David Grossman and Ariel Lavi for discussions during their REU research. This work was partly supported by a Google Faculty Research Award, the National Science Foundation (grant 1715475), and the National Institutes of Health (grant 1R01EY024858-01A1).",

year = "2020",

month = jul,

doi = "10.1167/JOV.20.7.21",

language = "English",

volume = "20",

number = "7",

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T1 - Deep neural networks capture texture sensitivity in V2

AU - Laskar, Md Nasir Uddin

AU - Giraldo, Luis Gonzalo Sanchez

AU - Schwartz, Odelia

N1 - Funding Information: The authors thank Adam Kohn, Ruben Coen Cagli, and Corey Ziemba for discussions and very helpful comments on an earlier version of the manuscript; Eero Simoncelli and Corey Ziemba for discussions and providing us with the original texture data set used in the experiments; and David Grossman and Ariel Lavi for discussions during their REU research. This work was partly supported by a Google Faculty Research Award, the National Science Foundation (grant 1715475), and the National Institutes of Health (grant 1R01EY024858-01A1).

PY - 2020/7

Y1 - 2020/7

KW - Deep learning

KW - Deep neural network

KW - Mid-level vision

KW - Texture perception

KW - Visual area V2

KW - Visual cortex

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U2 - 10.1167/JOV.20.7.21

DO - 10.1167/JOV.20.7.21

M3 - Article

C2 - 32692830

AN - SCOPUS:85088525122

VL - 20

JO - Journal of Vision

JF - Journal of Vision

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ER -

Deep neural networks capture texture sensitivity in V2

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

Access to Document

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