Heterogeneous domain adaptation (HDA) transfers knowledge across source and
target domains that present heterogeneities e.g., distinct domain distributions
and difference in feature type or dimension. Most previous HDA methods tackle
this problem through learning a domain-invariant feature subspace to reduce the
discrepancy between domains. However, the intrinsic semantic properties
contained in data are under-explored in such alignment strategy, which is also
indispensable to achieve promising adaptability. In this paper, we propose a
Simultaneous Semantic Alignment Network (SSAN) to simultaneously exploit
correlations among categories and align the centroids for each category across
domains. In particular, we propose an implicit semantic correlation loss to
transfer the correlation knowledge of source categorical prediction
distributions to target domain. Meanwhile, by leveraging target pseudo-labels,
a robust triplet-centroid alignment mechanism is explicitly applied to align
feature representations for each category. Notably, a pseudo-label refinement
procedure with geometric similarity involved is introduced to enhance the
target pseudo-label assignment accuracy. Comprehensive experiments on various
HDA tasks across text-to-image, image-to-image and text-to-text successfully
validate the superiority of our SSAN against state-of-the-art HDA methods. The
code is publicly available at this https URL

本文提出了一种同时利用语义相似性和目标伪标签的异构域适应模型（SSAN），通过隐式语义相关损失和几何相似性参与的伪标签精化过程，在不同数据类型的异构域上实现有效的特征对齐和类别中心对齐，Excel 在各种异构域适应任务中。