Sparsely-Labeled Source Assisted Domain Adaptation

• URL: http://arxiv.org/abs/2005.04111v1
• Date: Fri, 8 May 2020 15:37:35 GMT
• Title: Sparsely-Labeled Source Assisted Domain Adaptation
• Authors: Wei Wang, Zhihui Wang, Yuankai Xiang, Jing Sun, Haojie Li, Fuming Sun, Zhengming Ding
• Abstract summary: This paper proposes a novel Sparsely-Labeled Source Assisted Domain Adaptation (SLSA-DA) algorithm. Due to the label scarcity problem, the projected clustering is conducted on both the source and target domains.
• Score: 64.75698236688729
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Domain Adaptation (DA) aims to generalize the classifier learned from the source domain to the target domain. Existing DA methods usually assume that rich labels could be available in the source domain. However, there are usually a large number of unlabeled data but only a few labeled data in the source domain, and how to transfer knowledge from this sparsely-labeled source domain to the target domain is still a challenge, which greatly limits their application in the wild. This paper proposes a novel Sparsely-Labeled Source Assisted Domain Adaptation (SLSA-DA) algorithm to address the challenge with limited labeled source domain samples. Specifically, due to the label scarcity problem, the projected clustering is conducted on both the source and target domains, so that the discriminative structures of data could be leveraged elegantly. Then the label propagation is adopted to propagate the labels from those limited labeled source samples to the whole unlabeled data progressively, so that the cluster labels are revealed correctly. Finally, we jointly align the marginal and conditional distributions to mitigate the cross-domain mismatch problem, and optimize those three procedures iteratively. However, it is nontrivial to incorporate those three procedures into a unified optimization framework seamlessly since some variables to be optimized are implicitly involved in their formulas, thus they could not promote to each other. Remarkably, we prove that the projected clustering and conditional distribution alignment could be reformulated as different expressions, thus the implicit variables are revealed in different optimization steps. As such, the variables related to those three quantities could be optimized in a unified optimization framework and facilitate to each other, to improve the recognition performance obviously.

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