Time and resource restrictions limit the architect's design scope. Computational design methods can offer support to overcome these limitations. Design exploration has been a long-established task in computational-aided generative design. However, conventional objective- and performance-based systems have restrictions pertaining to the exploration scope. Without a quasi-global cognition of the conceptual design space, the exploration scope is bound to be limited. This paper is a proposal for an epistemic shift toward the interpretation of conceptual design space per se. This topic receives limited attention in the current literature due to the scarcity of interpretation tools. Using a customized large-scale architectural image database with high-level visual diversity and latent data space coverage, this paper serves as a first attempt to investigate the possibilities of leveraging disentangled representation learning to structurally interpret architectural conceptual design space in both supervised and unsupervised manner. Various schemes of supervised disentanglement are tested, with analytical comparisons indicating discrepant structural traits of different latent spaces. The unsupervised interpretation scheme shows the preliminary capability of automatic feature disentanglement. Our long-term objective is to offer designers a broader spectrum of creative design through innovative design systems.

中文翻译：

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破译嘈杂的景观：使用分离表示学习的建筑概念设计空间解释

时间和资源限制限制了架构师的设计范围。计算设计方法可以为克服这些限制提供支持。设计探索一直是计算辅助生成设计中的一项长期任务。然而，传统的基于目标和性能的系统对勘探范围有限制。如果没有对概念设计空间的准全局认知，探索范围势必会受到限制。本文是对概念设计空间本身的解释进行认知转变的提议。由于缺乏解释工具，该主题在当前文献中受到的关注有限。使用具有高度视觉多样性和潜在数据空间覆盖的定制大型建筑图像数据库，本文首次尝试研究利用分离表示学习以有监督和无监督的方式在结构上解释建筑概念设计空间的可能性。测试了各种监督解缠方案，分析比较表明不同潜在空间的结构特征存在差异。无监督解释方案显示了自动特征分离的初步能力。我们的长期目标是通过创新的设计系统为设计师提供更广泛的创意设计。分析比较表明不同潜在空间的不同结构特征。无监督解释方案显示了自动特征分离的初步能力。我们的长期目标是通过创新的设计系统为设计师提供更广泛的创意设计。分析比较表明不同潜在空间的不同结构特征。无监督解释方案显示了自动特征分离的初步能力。我们的长期目标是通过创新的设计系统为设计师提供更广泛的创意设计。