Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition
Identifieur interne : 003B05 ( Ncbi/Checkpoint ); précédent : 003B04; suivant : 003B06Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition
Auteurs : Saeed Reza Kheradpisheh [Iran, France] ; Masoud Ghodrati ; Mohammad Ganjtabesh [Iran] ; Timothée Masquelier [France]Source :
- Scientific Reports [ 2045-2322 ] ; 2016.
Abstract
Deep convolutional neural networks (DCNNs) have attracted much attention recently, and have shown to be able to recognize thousands of object categories in natural image databases. Their architecture is somewhat similar to that of the human visual system: both use restricted receptive fields, and a hierarchy of layers which progressively extract more and more abstracted features. Yet it is unknown whether DCNNs match human performance at the task of view-invariant object recognition, whether they make similar errors and use similar representations for this task, and whether the answers depend on the magnitude of the viewpoint variations. To investigate these issues, we benchmarked eight state-of-the-art DCNNs, the HMAX model, and a baseline shallow model and compared their results to those of humans with backward masking. Unlike in all previous DCNN studies, we carefully controlled the magnitude of the viewpoint variations to demonstrate that shallow nets can outperform deep nets and humans when variations are weak. When facing larger variations, however, more layers were needed to match human performance and error distributions, and to have representations that are consistent with human behavior. A very deep net with 18 layers even outperformed humans at the highest variation level, using the most human-like representations.
Url:
DOI: 10.1038/srep32672
PubMed: 27601096
PubMed Central: 5013454
Affiliations:
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last="Kheradpisheh">Saeed Reza Kheradpisheh</name><affiliation wicri:level="1"><nlm:aff id="a1"><institution>Department of Computer Science, School of Mathematics, Statistics, and Computer Science, University of Tehran</institution>, Tehran,<country>Iran</country></nlm:aff><country xml:lang="fr">Iran</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="a2"><institution>CERCO UMR 5549, CNRS – Université de Toulouse</institution>, F-31300,<country>France</country></nlm:aff><country xml:lang="fr">France</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation></author><author><name sortKey="Ghodrati, Masoud" sort="Ghodrati, Masoud" uniqKey="Ghodrati M" first="Masoud" last="Ghodrati">Masoud Ghodrati</name><affiliation><nlm:aff id="a3"><institution>Department of Physiology, Monash University</institution>, Clayton,<country>Australia 3800</country></nlm:aff><wicri:noCountry code="nlm 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F-31300,<country>France</country></nlm:aff><country xml:lang="fr">France</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="a5"><institution>INSERM, U968</institution>, Paris, F-75012,<country>France</country></nlm:aff><country xml:lang="fr">France</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="a6"><institution>Sorbonne Universités, UPMC Univ Paris 06, UMR-S 968, Institut de la Vision</institution>, Paris, F-75012,<country>France</country></nlm:aff><country xml:lang="fr">France</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="a7"><institution>CNRS, UMR-7210</institution>, Paris, F-75012,<country>France</country></nlm:aff><country xml:lang="fr">France</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation></author></analytic><series><title level="j">Scientific Reports</title><idno type="eISSN">2045-2322</idno><imprint><date when="2016">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Deep convolutional neural networks (DCNNs) have attracted much attention recently, and have shown to be able to recognize thousands of object categories in natural image databases. Their architecture is somewhat similar to that of the human visual system: both use restricted receptive fields, and a hierarchy of layers which progressively extract more and more abstracted features. Yet it is unknown whether DCNNs match human performance at the task of view-invariant object recognition, whether they make similar errors and use similar representations for this task, and whether the answers depend on the magnitude of the viewpoint variations. To investigate these issues, we benchmarked eight state-of-the-art DCNNs, the HMAX model, and a baseline shallow model and compared their results to those of humans with backward masking. Unlike in all previous DCNN studies, we carefully controlled the magnitude of the viewpoint variations to demonstrate that shallow nets can outperform deep nets and humans when variations are weak. When facing larger variations, however, more layers were needed to match human performance and error distributions, and to have representations that are consistent with human behavior. A very deep net with 18 layers even outperformed humans at the highest variation level, using the most human-like representations.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Dicarlo, J J" uniqKey="Dicarlo J">J. J. DiCarlo</name></author><author><name sortKey="Zoccolan, D" uniqKey="Zoccolan D">D. Zoccolan</name></author><author><name sortKey="Rust, N C" uniqKey="Rust N">N. C. Rust</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Dicarlo, J J" uniqKey="Dicarlo J">J. J. DiCarlo</name></author><author><name sortKey="Cox, D D" uniqKey="Cox D">D. D. Cox</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Liu, H" uniqKey="Liu H">H. 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Ebrahimpour</name></author><author><name sortKey="Khaligh Razavi, S M" uniqKey="Khaligh Razavi S">S.-M. Khaligh-Razavi</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Tsao, D Y" uniqKey="Tsao D">D. Y. Tsao</name></author><author><name sortKey="Freiwald, W A" uniqKey="Freiwald W">W. A. Freiwald</name></author><author><name sortKey="Knutsen, T A" uniqKey="Knutsen T">T. A. Knutsen</name></author><author><name sortKey="Mandeville, J B" uniqKey="Mandeville J">J. B. Mandeville</name></author><author><name sortKey="Tootell, R B" uniqKey="Tootell R">R. B. Tootell</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Tsao, D Y" uniqKey="Tsao D">D. Y. Tsao</name></author><author><name sortKey="Freiwald, W A" uniqKey="Freiwald W">W. A. Freiwald</name></author><author><name sortKey="Tootell, R B" uniqKey="Tootell R">R. B. Tootell</name></author><author><name sortKey="Livingstone, M S" uniqKey="Livingstone M">M. S. Livingstone</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>France</li><li>Iran</li></country></list><tree><noCountry><name sortKey="Ghodrati, Masoud" sort="Ghodrati, Masoud" uniqKey="Ghodrati M" first="Masoud" last="Ghodrati">Masoud Ghodrati</name></noCountry><country name="Iran"><noRegion><name sortKey="Kheradpisheh, Saeed Reza" sort="Kheradpisheh, Saeed Reza" uniqKey="Kheradpisheh S" first="Saeed Reza" last="Kheradpisheh">Saeed Reza Kheradpisheh</name></noRegion><name sortKey="Ganjtabesh, Mohammad" sort="Ganjtabesh, Mohammad" uniqKey="Ganjtabesh M" first="Mohammad" last="Ganjtabesh">Mohammad Ganjtabesh</name></country><country name="France"><noRegion><name sortKey="Kheradpisheh, Saeed Reza" sort="Kheradpisheh, Saeed Reza" uniqKey="Kheradpisheh S" first="Saeed Reza" last="Kheradpisheh">Saeed Reza Kheradpisheh</name></noRegion><name sortKey="Masquelier, Timothee" sort="Masquelier, Timothee" uniqKey="Masquelier T" first="Timothée" last="Masquelier">Timothée Masquelier</name><name sortKey="Masquelier, Timothee" sort="Masquelier, Timothee" uniqKey="Masquelier T" first="Timothée" last="Masquelier">Timothée Masquelier</name><name sortKey="Masquelier, Timothee" sort="Masquelier, Timothee" uniqKey="Masquelier T" first="Timothée" last="Masquelier">Timothée Masquelier</name><name sortKey="Masquelier, Timothee" sort="Masquelier, Timothee" uniqKey="Masquelier T" first="Timothée" last="Masquelier">Timothée Masquelier</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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