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.

翻译：生成对抗网络（GANs）在图像编辑、领域转换、缺失数据填补以及创意工作支持等多个应用领域具有重要价值。然而，GANs常被视为"黑箱"，具体而言，最终用户几乎无法控制如何通过解缠来改进编辑方向。现有研究主要聚焦于通过设计新型GAN架构来实现编辑方向的解缠。为此，我们提出GANravel——一种用户驱动的方向解缠工具，它能补充现有GAN架构的功能，使用户能够迭代地改进编辑方向。在两项各有16名参与者的用户研究中，使用GANravel的用户能够成功实现方向解缠，并在解缠性能上超越了最先进的基线方向发现方法。在第二项用户研究中，GANravel被应用于创作狗表情包的创意任务，能够生成高质量的编辑图像与GIF动画。