| Autor: |
Lida Abdi, Sattar Hashemi |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 9, Pp 127054-127067 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2021.3111586 |
| Popis: |
This research proposes a novel unsupervised domain adaptation algorithm for cross-domain visual recognition. Distance Correlation-based Domain Adaptation or DCDA algorithm is developed by a correlation measure, called distance correlation. DCDA exploits both the statistical and geometrical properties of the data while embedding the both domain instances to a latent feature space. Unlike many proposed algorithms in the literature that utilize the source domain labels to learn pseudo labels, DCDA further exploits the available information in the source domain labels to discover an appropriate projection operator. The implementation of the proposed DCDA algorithm is easy, and it has a closed-form solution. Our experiments and analyses of the results over a wide variety of benchmark domain adaptation data sets indicate that DCDA has significantly better results in comparison with other state-of-the-art approaches in unsupervised domain adaptation and deep learning literature. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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