  Deep domain adaptation methods can reduce the distribution discrepancy by
learning domain-invariant embedddings. However, these methods only focus on
aligning the whole data distributions, without considering the class-level
relations among source and target images. Thus, a target embeddings of a bird
might be aligned to source embeddings of an airplane. This semantic
misalignment can directly degrade the classifier performance on the target
dataset. To alleviate this problem, we present a similarity constrained
alignment (SCA) method for unsupervised domain adaptation. When aligning the
distributions in the embedding space, SCA enforces a similarity-preserving
constraint to maintain class-level relations among the source and target
images, i.e., if a source image and a target image are of the same class label,
their corresponding embeddings are supposed to be aligned nearby, and vise
versa. In the absence of target labels, we assign pseudo labels for target
images. Given labeled source images and pseudo-labeled target images, the
similarity-preserving constraint can be implemented by minimizing the triplet
loss. With the joint supervision of domain alignment loss and
similarity-preserving constraint, we train a network to obtain domain-invariant
embeddings with two critical characteristics, intra-class compactness and
inter-class separability. Extensive experiments conducted on the two datasets
well demonstrate the effectiveness of SCA.

Computer Vision and Pattern Recognition