GANalyze: Toward Visual Definitions of Cognitive Image Properties

Lore Goetschalckx, Alex Andonian, Aude Oliva, Phillip Isola; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 5744-5753

Abstract


We introduce a framework that uses Generative Adversarial Networks (GANs) to study cognitive properties like memorability. These attributes are of interest because we do not have a concrete visual definition of what they entail. What does it look like for a dog to be more memorable? GANs allow us to generate a manifold of natural-looking images with fine-grained differences in their visual attributes. By navigating this manifold in directions that increase memorability, we can visualize what it looks like for a particular generated image to become more memorable. The resulting "visual definitions" surface image properties (like "object size") that may underlie memorability. Through behavioral experiments, we verify that our method indeed discovers image manipulations that causally affect human memory performance. We further demonstrate that the same framework can be used to analyze image aesthetics and emotional valence. ganalyze.csail.mit.edu.

Related Material


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[bibtex]
@InProceedings{Goetschalckx_2019_ICCV,
author = {Goetschalckx, Lore and Andonian, Alex and Oliva, Aude and Isola, Phillip},
title = {GANalyze: Toward Visual Definitions of Cognitive Image Properties},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}