Generating preferred images using generative adversarial networks (GANs) is challenging owing to the high-dimensional nature of latent space. In this study, we propose a novel approach that uses simple user-swipe interactions to generate preferred images for users. To effectively explore the latent space with only swipe interactions, we apply principal component analysis to the latent space of the StyleGAN, creating meaningful subspaces. We use a multi-armed bandit algorithm to decide the dimensions to explore, focusing on the preferences of the user. Experiments show that our method is more efficient in generating preferred images than the baseline methods. Furthermore, changes in preferred images during image generation or the display of entirely different image styles were observed to provide new inspirations, subsequently altering user preferences. This highlights the dynamic nature of user preferences, which our proposed approach recognizes and enhances.

使用生成对抗网络(GANs)生成偏好图像具有挑战性，因为潜在空间的高维性质。本研究提出了一种新方法，利用简单的用户滑动交互来生成用户偏好图像。为了有效地利用仅滑动交互的潜在空间，我们对StyleGAN的潜在空间应用主成分分析，创建有意义的子空间。我们使用多臂老虎机算法来决定要探索的维度，着重关注用户的偏好。实验证明，与基准方法相比，我们的方法在生成用户偏好图像方面更高效。此外，观察到在图像生成过程中偏好图像的变化或完全不同的图像风格的显示提供了新的灵感，从而改变了用户的偏好。这凸显了用户偏好的动态性，而我们提出的方法能够认识并增强这种动态性。

SwipeGANSpace：通过高效的潜空间探索实现滑动比较图像生成