Rethinking Triplet Loss for Domain Adaptation

Weijian Deng, Liang Zheng, Yifan Sun, Jianbin Jiao

    Research output: Contribution to journalArticlepeer-review

    63 Citations (Scopus)

    Abstract

    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 and target domains. From a micro perspective, by enforcing this constraint on every possible triplet, images from different domains but of the same class are mapped nearby, and those of different classes are far apart. From a macro perspective, our method ensures that cross-domain similarities are preserved, leading to intra-class compactness and inter-class separability. Extensive experiment on four datasets shows our method yields significant improvement over the baselines and has a competitive accuracy with the state-of-the-art results.

    Original languageEnglish
    Article number8964455
    Pages (from-to)29-37
    Number of pages9
    JournalIEEE Transactions on Circuits and Systems for Video Technology
    Volume31
    Issue number1
    DOIs
    Publication statusPublished - Jan 2021

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