Joint Geometrical and Statistical Alignment for Visual Domain Adaptation
It addresses the problem of domain shift in visual recognition for applications like computer vision, but it is incremental as it builds on existing alignment methods.
The paper tackles unsupervised domain adaptation for cross-domain visual recognition by proposing a unified framework that reduces both statistical and geometrical shifts between domains, achieving significant performance improvements over state-of-the-art methods on synthetic and real-world datasets.
This paper presents a novel unsupervised domain adaptation method for cross-domain visual recognition. We propose a unified framework that reduces the shift between domains both statistically and geometrically, referred to as Joint Geometrical and Statistical Alignment (JGSA). Specifically, we learn two coupled projections that project the source domain and target domain data into low dimensional subspaces where the geometrical shift and distribution shift are reduced simultaneously. The objective function can be solved efficiently in a closed form. Extensive experiments have verified that the proposed method significantly outperforms several state-of-the-art domain adaptation methods on a synthetic dataset and three different real world cross-domain visual recognition tasks.