  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.
