Joint Geometrical and Statistical Alignment for Visual Domain Adaptation

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

Mind the Gap: Subspace based Hierarchical Domain Adaptation

Domain adaptation techniques aim at adapting a classifier learnt on a source domain to work on the target domain. Exploiting the subspaces spanned by features of the source and target domains respectively is one approach that has been investigated towards solving this problem. These techniques normally assume the existence of a single subspace for the entire source / target domain. In this work, we consider the hierarchical organization of the data and consider multiple subspaces for the source and target domain based on the hierarchy. We evaluate different subspace based domain adaptation techniques under this setting and observe that using different subspaces based on the hierarchy yields consistent improvement over a non-hierarchical baselineComment: 4 pages in Second Workshop on Transfer and Multi-Task Learning: Theory meets Practice in NIPS 201