2 research outputs found
Simultaneous Semantic Alignment Network for Heterogeneous Domain Adaptation
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 https://github.com/BIT-DA/SSAN.Comment: Accepted at ACM MM 202
Heterogeneous Domain Adaptation via Soft Transfer Network
Heterogeneous domain adaptation (HDA) aims to facilitate the learning task in
a target domain by borrowing knowledge from a heterogeneous source domain. In
this paper, we propose a Soft Transfer Network (STN), which jointly learns a
domain-shared classifier and a domain-invariant subspace in an end-to-end
manner, for addressing the HDA problem. The proposed STN not only aligns the
discriminative directions of domains but also matches both the marginal and
conditional distributions across domains. To circumvent negative transfer, STN
aligns the conditional distributions by using the soft-label strategy of
unlabeled target data, which prevents the hard assignment of each unlabeled
target data to only one category that may be incorrect. Further, STN introduces
an adaptive coefficient to gradually increase the importance of the soft-labels
since they will become more and more accurate as the number of iterations
increases. We perform experiments on the transfer tasks of image-to-image,
text-to-image, and text-to-text. Experimental results testify that the STN
significantly outperforms several state-of-the-art approaches.Comment: Accepted by ACM Multimedia (ACM MM) 201