Unsupervised Domain Adaptation via Discriminative Manifold Propagation
- URL: http://arxiv.org/abs/2008.10030v1
- Date: Sun, 23 Aug 2020 12:31:37 GMT
- Title: Unsupervised Domain Adaptation via Discriminative Manifold Propagation
- Authors: You-Wei Luo, Chuan-Xian Ren, Dao-Qing Dai and Hong Yan
- Abstract summary: Unsupervised domain adaptation is effective in leveraging rich information from a labeled source domain to an unlabeled target domain.
The proposed method can be used to tackle a series of variants of domain adaptation problems, including both vanilla and partial settings.
- Score: 26.23123292060868
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Unsupervised domain adaptation is effective in leveraging rich information
from a labeled source domain to an unlabeled target domain. Though deep
learning and adversarial strategy made a significant breakthrough in the
adaptability of features, there are two issues to be further studied. First,
hard-assigned pseudo labels on the target domain are arbitrary and error-prone,
and direct application of them may destroy the intrinsic data structure.
Second, batch-wise training of deep learning limits the characterization of the
global structure. In this paper, a Riemannian manifold learning framework is
proposed to achieve transferability and discriminability simultaneously. For
the first issue, this framework establishes a probabilistic discriminant
criterion on the target domain via soft labels. Based on pre-built prototypes,
this criterion is extended to a global approximation scheme for the second
issue. Manifold metric alignment is adopted to be compatible with the embedding
space. The theoretical error bounds of different alignment metrics are derived
for constructive guidance. The proposed method can be used to tackle a series
of variants of domain adaptation problems, including both vanilla and partial
settings. Extensive experiments have been conducted to investigate the method
and a comparative study shows the superiority of the discriminative manifold
learning framework.
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