固有图像分解（IID）是一个不受限制的问题。因此，传统方法使用手工制作的先验来限制问题。但是，在应对复杂场景时，这些约束受到限制。基于深度学习的方法通过数据隐含地学习了这些约束，但是它们通常会遭受数据集偏见的困扰（由于无法包括所有可能的成像条件）。在本文中，提出了两者的组合。利用语义和不变特征（例如语义和不变特征）以获得语义和物理上合理的反射率转换。这些过渡用于引导具有隐式同质性约束的进行性CNN，以分解反射率和阴影图。进行了一项消融研究，表明拟议的先验和进行性CNN的使用增加了IID的性能。我们提出的数据集和标准现实世界IIW数据集的最新性能都显示了提出的方法的有效性。代码可在https://github.com/morpheus3000/signet上提供

Training of a Machine Learning model requires sufficient data. The sufficiency of the data is not always about the quantity, but about the relevancy and reduced redundancy. Data-generating processes create massive amounts of data. When used raw, such big data is causing much computational resource utilization. Instead of using the raw data, a proper Condensed Representation can be used instead. Combining K-means, a well-known clustering method, with some correction and refinement facilities a novel Condensed Representation method for Machine Learning applications is introduced. To present the novel method meaningfully and visually, synthetically generated data is employed. It has been shown that by using the condensed representation, instead of the raw data, acceptably accurate model training is possible.