Maximum likelihood weight estimation for partial domain adaptation

Abstract

Partial Domain Adaptation (PDA) aims to generalize a classification model from a labeled source domain to an unlabeled target domain, where the source label space contains the target label space. There are two main challenges in PDA that weaken the model's classification performance in the target domain: (i) the joint distribution of the source domain is related but different from that of the target domain, and (ii) the source outlier data, whose labels do not belong to the target label space, have a negative impact on learning the target classification model. To tackle these challenges, we propose a Maximum Likelihood Weight Estimation (MLWE) approach to estimate a weight function for the source domain. The weight function matches the joint source distribution of the relevant part to the joint target distribution, and reduces the negative impact of the source outlier data. To be specific, our approach estimates the weight function by maximizing a likelihood function, and the estimation leads to a nice convex optimization problem that has a global optimal solution. In the experiments, our approach demonstrates superior performance on popular benchmark datasets. Intro video and PyTorch code are available at https://github.com/sentaochen/Maximum-Likelihood-Weight-Estimation.