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The goal of domain adaptation is to train a high-performance predictive model on the target domain data by using knowledge from the source domain data, which has different but related data distribution. In this paper, we consider unsupervised domain adaptation where we have labelled source domain data but unlabelled target domain data. Our solution to unsupervised domain adaptation is to learn a domain- invariant representation that is also category discriminative. Domain- invariant representations are realized by minimizing the domain discrepancy. To minimize the domain discrepancy, we propose a novel graph- matching metric between the source and target domain representations. Minimizing this metric allows the source and target representations to be in support of each other. We further exploit confident unlabelled target domain samples and their pseudo-labels to refine our proposed model. We expect the refining step to improve the performance further. This is validated by performing experiments on standard image classification adaptation datasets. Results showed our proposed approach out-perform previous domain-invariant representation learning approaches.
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Glocal Alignment for Unsupervised Domain Adaptation
Traditional unsupervised domain adaptation methods attempt to align source and target domains globally and are agnostic to the categories of the data points. This results in an inaccurate categorical alignment and diminishes the classification performance on the target domain. In this paper, we alter existing adversarial domain alignment methods to adhere to category alignment by imputing category information. We partition the samples based on category using source labels and target pseudo labels and then apply domain alignment for every category. Our proposed modification provides a boost in performance even with a modest pseudo label estimator. We evaluate our approach on 4 popular domain alignment loss functions using object recognition and digit datasets.
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- Award ID(s):
- 1828010
- PAR ID:
- 10344390
- Date Published:
- Journal Name:
- MULL 2021 - Proceedings of the 1st Workshop on Multimedia Understanding with Less Labeling, co-located with ACM MM 2021
- Page Range / eLocation ID:
- 45 to 51
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3476098.3485051
