GANravel: User-Driven Direction Disentanglement in Generative Adversarial Networks
arXiv:2302.00079 · doi:10.1145/3544548.3581226
Abstract
Generative adversarial networks (GANs) have many application areas including image editing, domain translation, missing data imputation, and support for creative work. However, GANs are considered 'black boxes'. Specifically, the end-users have little control over how to improve editing directions through disentanglement. Prior work focused on new GAN architectures to disentangle editing directions. Alternatively, we propose GANravel a user-driven direction disentanglement tool that complements the existing GAN architectures and allows users to improve editing directions iteratively. In two user studies with 16 participants each, GANravel users were able to disentangle directions and outperformed the state-of-the-art direction discovery baselines in disentanglement performance. In the second user study, GANravel was used in a creative task of creating dog memes and was able to create high-quality edited images and GIFs.
References in corpus (5)
- Learning Transferable Visual Models From Natural Language Supervision
- Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis
- Controlling generative models with continuous factors of variations
- GANSlider: How Users Control Generative Models for Images using Multiple Sliders with and without Feedforward Information
- GANzilla: User-Driven Direction Discovery in Generative Adversarial Networks