TY - JOUR
T1 - Rethinking Triplet Loss for Domain Adaptation
AU - Deng, Weijian
AU - Zheng, Liang
AU - Sun, Yifan
AU - Jiao, Jianbin
N1 - Publisher Copyright:
© 1991-2012 IEEE.
PY - 2021/1
Y1 - 2021/1
N2 - 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.
AB - 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.
KW - Domain adaptation
KW - semantic alignment
KW - triplet loss
UR - http://www.scopus.com/inward/record.url?scp=85099449592&partnerID=8YFLogxK
U2 - 10.1109/TCSVT.2020.2968484
DO - 10.1109/TCSVT.2020.2968484
M3 - Article
SN - 1051-8215
VL - 31
SP - 29
EP - 37
JO - IEEE Transactions on Circuits and Systems for Video Technology
JF - IEEE Transactions on Circuits and Systems for Video Technology
IS - 1
M1 - 8964455
ER -