This paper explores the challenges of converting architectural floor plans from raster to vector images. Unlike previous studies, our research focuses on domain adaptation to address stylistic and technical variations across different floor plan datasets. We develop and test our vectorization method on the CubiCasa5K benchmark, which includes 3 different floor plan styles. Our analysis reveals differences in input features across the CubiCasa5K styles, indicating the potential for domain adaptation research, mostly in room segmentation. However, we also find multiple indications that labelling in the CubiCasa5K dataset is ambiguous and inconsistent. Furthermore, styles with more training data do not always perform better, highlighting the complexity differences between floor plan styles. Our baseline shows a 0.7% gap for rooms yet a 0.6% improvement for objects, likely caused by the smaller feature gaps and inconsistent labelling. To address the adaptation gap, we add a Multi-Kernel Maximum Mean Discrepancy (MK-MMD) loss to the CubiCasa5K model to minimize feature distribution differences between domains. While our MK-MMD implementation shows potential for reducing the adaptation gap, persistence issues and mixed results across classes make it difficult to draw clear conclusions. Our findings also show the role of balancing spatial context in the MK-MMD calculation. These insights lay a foundation for future domain adaptation research in floor plan vectorization.

Generalizing models for new unknown datasets is a common problem in machine learning. Algorithms that perform well for test instances with the same distribution as their training dataset often perform severely on new datasets with a different distribution. This problem is caused by distributional shifts between the training of the model and applying that model to a test domain. This paper addresses whether and in what situations Risk Extrapolation (REx) can tackle this problem of Out-Of-Distribution generalization by exploiting invariant relationships. These relationships are based on features that are invariant across all domains. By learning these relationships, REx aims to learn the concept of the problem we are trying to solve. We show in what situations REx can learn these invariant relationships and when it does not. We translate the definition of an invariant relationship into a homoscedastic synthetic dataset with either covariate, confounded, anti-causal, or hybrid shift. We expose REx to experiments in sample complexity, the number of training domains, and the training domain distance. We show that REx performs better for invariant prediction in situations with larger sample sizes and training domain distance and that if these criteria are met, REx performs equivalently in all four distributional shifts. We also compare REx to Invariant- and Empirical Risk Minimization and show that; REx is less sensitive and thus robust to the shifting of the average distributional variance in the training domains; REx asymptotically out-performs the methods in the more complex distributional shifts.