Domain adaptation for object recognition using subspace sampling demons

Manually labeling data for training machine learning models is time-consuming and expensive. Therefore, it is often necessary to apply models built in one domain to a new domain. However, existing approaches do not evaluate the quality of intermediate features that are learned in the process of transferring from the source domain to the target domain, which results in the potential for sub-optimal features. Also, transfer learning models in existing work do not provide optimal results for a new domain. In this paper, we first propose a fast subspace sampling demons (SSD) method to learn intermediate subspace features from two domains and then evaluate the quality of the learned features. To show the applicability of our model, we test our model using a synthetic dataset as well as several benchmark datasets. Extensive experiments demonstrate significant improvements in classification accuracy over the state of the art.