查看文章

The gap in data distribution motivates domain adaptation research. In this area, image classification intrinsically requires the source and target features to be co-located if they are of the same class. However, many works only take a global view of the domain gap. That is, to make the data distributions globally overlap; and this does not necessarily lead to feature co-location at the class level. To resolve this problem, we study metric learning in the context of domain adaptation. Specifically, we introduce a similarity guided constraint (SGC). In the implementation, SGC takes the form of a triplet loss. The triplet loss is integrated into the network as an additional objective term. Here, an image triplet consists of two images of the same class and another image of a different class. Albeit simple, the working mechanism of our method is interesting and insightful. Importantly, images in the triplets are sampled from the source …