Latent Space Climber: Progressive Latent Space Exploration for Desirable Sample Discovery
Hongyang Wang , Zhangnan Wang , Jie Li
Generative Model (GM)-assisted product design has become increasingly popular. The key is to find generated samples (GSs) satisfying the design goal from the GM’s latent space (GLS). Most existing works rely on defining objective functions or writing prompts to search for GSs in the GLS. Unlike them, we propose a progressive approach that relies on multiple rounds of neighborhood exploration to choose desirable GSs from the GLS. Notably, the approach allows users to concretize and refine their goals during the exploration, thus applying to abstract or unspecific goals, which is unavailable for all existing techniques. The approach integrates two techniques to solve the challenges of achieving and applying it. First, many GSs are highly similar or irrelevant to the neighborhood center. Those GSs do not allow users to make comprehensive comparisons for rational choices and cannot be excluded by classic methods. Thus, we proposed a method to avoid collecting them, which makes collected GSs have representative feature variations from the neighborhood center. Second, we need a system for applying the approach. The system should fulfill many visualization requirements to efficiently drive exploration and keep it always in the right direction. Thus, we followed the mountain-climbing metaphor to design the system and developed a series of visual and quantitative techniques to achieve these requirements. Cases on multiple real-world datasets and GMs, results of quantitative experiments, and performance and feedback of participants in user studies prove the approach’s effectiveness and usability.
latent space / desirable sample / product design / generative model / GANs / visualization / interactive exploration
The Author(s) 2026.
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