Visual Domain Adaptation Exploiting Confidence-Samples

Domain adaptation methods are used to address a problem, in which train scenario (source domain) and test scenario (target domain) are different. The existing methods mainly perform adaptation via reducing domain discrepancy from the view of a probability distribution. However, the idea of probability distribution matching always leads to a complex optimization process. Thereby these methods are difficult to apply in some scenario like online application or fast perception in dynamic environments. In this paper, we propose a new and simple domain adaptation method that utilizes confidence- samples to facilitate the classifier training on the target domain. Here, the confidence-samples are a subset of the target samples, and they have very credibly predicted labels. In order to detect the samples, a Category Similarity Collaborative Representation (CSCR) is first developed, by which the raw labels of all target samples are predicted using the smallest projection error according to the law of category. After this, the confidence score of the raw predicted labels is evaluated by the energy context information of CSCR. Finally, the target samples with a high confidence score are selected. Because of the linearity of CSCR, our method avoids complex optimization for matching the probability distribution. Empirical studies on a standard dataset demonstrate the advantages of our method.