As applications of generative AI become mainstream, it is important to understand what generative models are capable of producing, and the extent to which one can predictably control their outputs. In this paper, we propose a visualization design, named Concept Lens, for jointly navigating the data distribution of a generative model, and concept manipulations supported by the model. Our work is focused on modern vision-based generative adversarial networks (GAN), and their learned latent spaces, wherein concept discovery has gained significant interest as a means of image manipulation. Concept Lens is designed to support users in understanding the diversity of a provided set of concepts, the relationship between concepts, and the suitability of concepts to give semantic controls for image generation. Key to our approach is the hierarchical grouping of concepts, generated images, and the associated joint exploration. We show how Concept Lens can reveal consistent semantic manipulations for editing images, while also serving as a diagnostic tool for studying the limitations and trade-offs of concept discovery methods.

翻译：随着生成式人工智能应用逐渐成为主流，理解生成模型能够产生何种输出以及人们能在多大程度上可预测地控制其输出变得至关重要。本文提出一种名为"概念透镜"的可视化设计方案，用于联合导航生成模型的数据分布以及该模型所支持的概念操作。我们的研究聚焦于基于视觉的现代生成对抗网络（GAN）及其学习到的潜在空间，其中概念发现作为一种图像操作方法已引起广泛关注。概念透镜旨在帮助用户理解给定概念集合的多样性、概念间的关系，以及概念在提供图像生成语义控制方面的适用性。我们方法的核心在于对概念、生成图像及相关联合探索进行层次化分组。我们展示了概念透镜如何揭示用于图像编辑的一致性语义操作，同时也可作为研究概念发现方法局限性与权衡的诊断工具。