Figure 1. Unsupervised domain adaptation in the context of population genetic inference.

A) A high-level overview of the supervised machine-learning approach for population genetic inference and how domain adaptation fits into the paradigm. B) Example formulations of the unsupervised domain adaptation problem with application to computer vision and population genetics. Note that in the specific case of SIA, which uses features of the ARG, the source domain data always consist of true genealogies generated in simulations, whereas the target domain data always consist of inferred genealogies reconstructed from observed sequence data. C) Four benchmarking scenarios considered in this study. The original model was both trained and tested on source domain data (simulation benchmark), both trained and tested on target domain data (hypothetical true model), or trained on source domain data but applied to target domain data (standard model application). These three cases contextualize the performance of the domain-adaptive model (see Methods for details). Gold squares represent source domain data, blue circles represent target domain data and crosses (x) represent labels.