The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
GANalyze: Toward Visual Definitions of Cognitive Image Properties
Open accessAbstract
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.
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Cites
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ImageNet Large Scale Visual Recognition Challenge
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Self-citationGAN（Generative Adversarial Nets）
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GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
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Cited by
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Shen, Y., Gu, J., Tang, X., & Zhou, B. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00926 · Cited by 1,063
GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields
Niemeyer, M., & Geiger, A. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01129 · Cited by 731
InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs
Shen, Y., Yang, C., Tang, X., & Zhou, B. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3034267 · Cited by 396
StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation
Wu, Z., Lischinski, D., & Shechtman, E. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01267 · Cited by 389
What Makes Fake Images Detectable? Understanding Properties that Generalize
Chai, L., Bau, D., Lim, S.-N., & Isola, P. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58574-7_7 · Cited by 357
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Metadata
- Authors
- Goetschalckx, L., Andonian, A., Oliva, A., & Isola, P.
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Volume
- 20
- Issue
- 11
- Pages
- 5743-5752
- OpenAlex
- W2950419363
- Cited by
- 283
- References
- 29
- FWCI
- 12.65
- Type
- conference paper
- Language
- en
- Open access
- gold
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