GANalyze: Toward Visual Definitions of Cognitive Image Properties
Author(s)Goetschalckx, Lore; Andonian, Alexander J; Oliva, Aude; Isola, Phillip John
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We introduce a framework that uses Generative Adversarial Networks (GANs) to study cognitive properties like memorability, aesthetics, and emotional valence. 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.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Goetschalckx, Lore et al. “GANalyze: Toward Visual Definitions of Cognitive Image Properties.” Paper in the Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 27 October-2 November 2019, Seoul, Korea, IEEE, © 2019 The Author(s)