LTF: A label transformation framework for correcting target shift

Abstract

Distribution shift is a major obstacle to the deployment of current deep learning models on real-world problems. Let Y be the target (label) and X the predictors (features). We focus on one type of distribution shift, target shift, where the marginal distribution of the target variable PY changes, but the conditional distribution PX|Y does not. Existing methods estimate the density ratio between the source- and target-domain label distributions by density matching. However, these methods are either computationally infeasible for large-scale data or restricted to shift correction for discrete labels. In this paper, we propose an end-to-end Label Transformation Framework (LTF) for correcting t..