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2017Zeitschriftenartikel DOI: 10.18452/18693
Dream Formulations and Deep Neural Networks: Humanistic Themes in the Iconology of the Machine-Learned Image
dc.contributor.authorSpratt, Emily L.
dc.date.accessioned2018-01-10T17:08:45Z
dc.date.available2018-01-10T17:08:45Z
dc.date.issued2017
dc.identifier.urihttp://edoc.hu-berlin.de/18452/19403
dc.description.abstractThis paper addresses the interpretability of deep learning-enabled image recognition processes in computer vision science in relation to theories in art history and cognitive psychology on the vision-related perceptual capabilities of humans. Examination of what is determinable about the machine-learned image in comparison to humanistic theories of visual perception, particularly in regard to art historian Erwin Panofsky’s methodology for image analysis and psychologist Eleanor Rosch’s theory of graded categorization according to prototypes, finds that there are surprising similarities between the two that suggest that researchers in the arts and the sciences would have much to benefit from closer collaborations. Utilizing the examples of Google’s DeepDream and the Machine Learning and Perception Lab at Georgia Tech’s Grad-CAM: Gradient-weighted Class Activation Mapping programs, this study suggests that a revival of art historical research in iconography and formalism in the age of AI is essential for shaping the future navigation and interpretation of all machine-learned images, given the rapid developments in image recognition technologies.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin
dc.rights(CC BY-SA 3.0 DE) Namensnennung - Weitergabe unter gleichen Bedingungen 3.0 Deutschlandger
dc.rights.urihttp://creativecommons.org/licenses/by-sa/3.0/de/
dc.subjectEmily L. Spratteng
dc.subjectMachine Learningeng
dc.subjectImage Classificationeng
dc.subjectMedia Theoryeng
dc.subjectIconographyeng
dc.subject.ddc700 Künste
dc.titleDream Formulations and Deep Neural Networks: Humanistic Themes in the Iconology of the Machine-Learned Image
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/19403-9
dc.identifier.doihttp://dx.doi.org/10.18452/18693
local.edoc.container-titleKunsttexte - Renaissance
local.edoc.pages15
local.edoc.type-nameZeitschriftenartikel
local.edoc.institutionPrinceton University
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
local.edoc.container-urlwww.kunsttexte.de
local.edoc.container-volume2017
local.edoc.container-issue4
local.edoc.container-periodicalpart-creatorAngela Dressen, Lia Markey
local.edoc.container-periodicalpart-titleCritical Approaches to Digital Art History
local.edoc.container-erstkatid2063498-5

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