Xiaobin Chang, Yongxin Yang, Tao Xiang, Timothy M. Hospedales
In this paper, a unified approach is presented to transfer learning that addresses several source and target domain label-space and annotation assumptions with a single model. It is particularly effective in handling a challenging case, where source and target label-spaces are disjoint, and outperforms alternatives in both unsupervised and semi-supervised settings. The key ingredient is a common representation termed Common Factorised Space. It is shared between source and target domains, and trained with an unsupervised factorisation loss and a graph-based loss. With a wide range of experiments, we demonstrate the flexibility, relevance and efficacy of our method, both in the challenging cases with disjoint label spaces, and in the more conventional cases such as unsupervised domain adaptation, where the source and target domains share the same label-sets.
| Task | Dataset | Metric | Value | Model |
|---|---|---|---|---|
| Domain Adaptation | Market to Duke | mAP | 27.3 | CFSM |
| Domain Adaptation | Market to Duke | rank-1 | 49.8 | CFSM |
| Domain Adaptation | Duke to Market | mAP | 28.3 | CFSM |
| Domain Adaptation | Duke to Market | rank-1 | 61.2 | CFSM |
| Unsupervised Domain Adaptation | Market to Duke | mAP | 27.3 | CFSM |
| Unsupervised Domain Adaptation | Market to Duke | rank-1 | 49.8 | CFSM |
| Unsupervised Domain Adaptation | Duke to Market | mAP | 28.3 | CFSM |
| Unsupervised Domain Adaptation | Duke to Market | rank-1 | 61.2 | CFSM |