This 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.Comment: 29 pages, 8 Figures, This paper was originally presented as Dream
Formulations and Image Recognition: Algorithms for the Study of Renaissance
Art, at Critical Approaches to Digital Art History, The Villa I Tatti, The
Harvard University Center for Italian Renaissance Studies and The Newberry
Center for Renaissance Studies, Renaissance Society of America Annual
Meeting, Chicago, 31 March 201