Interpretable domain adaptation using unsupervised feature selection on pre-trained source models

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We study a realistic domain adaptation setting where one has access to an already existing “black-box” machine learning model. Indeed, in real-life scenarios, an efficient pre-trained source domain predictive model is often available and required to be preserved. The solution we propose to this problem has the asset of providing an interpretable target to source transformation by seeking a sparse and ordered coordinate-wise adaptation of the feature space in addition to elementary mapping functions. To automatically select the subset of features to be adapted, we first introduce a weakly-supervised process relying on scarce labeled target data. Then, we address a more challenging unsupervised version of this domain adaptation scenario. To this end, we propose a new pseudo-label estimator over unlabeled target examples, which is based on rank-stability in regards to the source model prediction. Such estimated “labels” are further used in a feature selection process to assess whether each feature needs to be transformed to achieve adaptation. We provide theoretical foundations of our method as well as an efficient implementation. Numerical experiments on real datasets show particularly encouraging results since approaching the supervised case, where one has access to labeled target samples.