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

Wei Jiang · Richard Davis · Kevin Gonyop Kim · Pierre Dillenbourg


Abstract:

For design professionals, one of the key markers of expertise is a coherent understanding of the design space in which their work is situated. Novices lack this understanding, and as a result, they are likely to suffer from “design fixation.” Our goal is to create a system to support novices in gaining a more complete, expert understanding of their domain’s design space by making use of deep generative models.

Our starting point was the StyleGAN model trained on the Feidegger dataset. We extended the model in a number of ways to better support the exploration of the GAN's latent space. First, we employed the SGD method to project images into latent space, then we added a pixel-level loss function which dramatically improved the ability to locate out-of-sample examples. Second, we implemented a method to generate high-quality images via text descriptions. To achieve this, we randomly sample images from the latent space and pass these along with the text description through a CLIP model to find the image which most closely matches the text. Third, we performed PCA on the latent space to identify semantically-meaningful directions and provide a simple means for the user to interpolate a design in these directions. Finally, we developed an intuitive interface to allow three images to be combined using style mixing.

We developed a graphical front-end web application that can support novices in exploring the full design space of a domain. This interface combines a number of methods from the literature into a single system, which provides a fun and intuitive way for novices to meaningfully explore the latent space of a GAN.