Multi-Partition Feature Alignment Network for Unsupervised Domain Adaptation

Abstract

In this paper, we present a novel unsupervised domain adaptation framework, Multi-Partition Feature Alignment Network, that learns a deep neural model for the target domain without the need for any supervision. Recent leading approaches for unsupervised domain adaptation are based on adversarial alignment. Aligning the global distribution of the domain representations via adversarial training does not guarantee the class-wise distribution alignment. The proposed approach is built on adversarial learning with the focus on carefully aligning class-wise domain representations. Our algorithm utilizes the pseudo-labels (the predicted labels) of the target features to stimulate class-wise alignment. As the pseudo-labels of individual target features can be erroneous, instead of iteratively aligning individual target samples, the proposed framework introduces a generic class-specific multi-partition alignment procedure that enables superior class-discriminative alignment of domain representations. The competitive performance of the proposed framework against state-of-the-art approaches over a wide variety of visual recognition tasks, namely, the digits classification task and the object recognition task, validates its effectiveness for unsupervised domain adaptation.