A Novel Framework for Multi-Source Domain Adaptation with Discriminative Feature Learning

Unsupervised domain adaptation deals with the scenario where there is scarcity of labeled data for the training of machine learning models. Traditionally, the knowledge of a learner is transferred from a source domain to a target domain in a conventional setting. In this paper, we propose a novel framework for Multi-Source Domain Adaptation which can train the model using multiple source domains and can generalize well on a target dataset. It mitigates the domain shift caused by different domains by (1) coordinating the statistical moments for all combinations of the source and target domain distributions and (2) by collaboratively extracting the deep features from the shared representation space. Extensive experiments have been performed for the task of image classification using different datasets widely varying in size and complexity in order to demonstrate the effectiveness of our approach compared to the naive source-combined domain adaptation setting.