GANs for All: Supporting Fun and Intuitive Exploration of GAN Latent Spaces

Wei Jiang, Richard Lee Davis, Kevin Gonyop Kim, Pierre Dillenbourg
Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, PMLR 176:292-296, 2022.

Abstract

We have developed a new tool that makes it possible for people with zero programming experience to intentionally and meaningfully explore the latent space of a GAN. We combine a number of methods from the literature into a single system that includes multiple functionalities: uploading and locating images in the latent space, image generation with text, visual style mixing, and intentional and intuitive latent space exploration. This tool was developed to provide a means for designers to explore the "design space" of their domains. Our goal was to create a system to support novices in gaining a more complete, expert understanding of their domain{’}s design space by lowering the barrier of entry to using deep generative models in creative practice.

Cite this Paper

BibTeX
@InProceedings{pmlr-v176-jiang22a, title = {GANs for All: Supporting Fun and Intuitive Exploration of GAN Latent Spaces}, author = {Jiang, Wei and Davis, Richard Lee and Kim, Kevin Gonyop and Dillenbourg, Pierre}, booktitle = {Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track}, pages = {292--296}, year = {2022}, editor = {Kiela, Douwe and Ciccone, Marco and Caputo, Barbara}, volume = {176}, series = {Proceedings of Machine Learning Research}, month = {06--14 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v176/jiang22a/jiang22a.pdf}, url = {https://proceedings.mlr.press/v176/jiang22a.html}, abstract = {We have developed a new tool that makes it possible for people with zero programming experience to intentionally and meaningfully explore the latent space of a GAN. We combine a number of methods from the literature into a single system that includes multiple functionalities: uploading and locating images in the latent space, image generation with text, visual style mixing, and intentional and intuitive latent space exploration. This tool was developed to provide a means for designers to explore the "design space" of their domains. Our goal was to create a system to support novices in gaining a more complete, expert understanding of their domain{’}s design space by lowering the barrier of entry to using deep generative models in creative practice.} }
Endnote
%0 Conference Paper %T GANs for All: Supporting Fun and Intuitive Exploration of GAN Latent Spaces %A Wei Jiang %A Richard Lee Davis %A Kevin Gonyop Kim %A Pierre Dillenbourg %B Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track %C Proceedings of Machine Learning Research %D 2022 %E Douwe Kiela %E Marco Ciccone %E Barbara Caputo %F pmlr-v176-jiang22a %I PMLR %P 292--296 %U https://proceedings.mlr.press/v176/jiang22a.html %V 176 %X We have developed a new tool that makes it possible for people with zero programming experience to intentionally and meaningfully explore the latent space of a GAN. We combine a number of methods from the literature into a single system that includes multiple functionalities: uploading and locating images in the latent space, image generation with text, visual style mixing, and intentional and intuitive latent space exploration. This tool was developed to provide a means for designers to explore the "design space" of their domains. Our goal was to create a system to support novices in gaining a more complete, expert understanding of their domain{’}s design space by lowering the barrier of entry to using deep generative models in creative practice.
APA
Jiang, W., Davis, R.L., Kim, K.G. & Dillenbourg, P.. (2022). GANs for All: Supporting Fun and Intuitive Exploration of GAN Latent Spaces. Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, in Proceedings of Machine Learning Research 176:292-296 Available from https://proceedings.mlr.press/v176/jiang22a.html.

Related Material