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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Anthony, Sicilia | - |
| dc.contributor.author | Xingchen, Zhao | - |
| dc.contributor.author | Seong Jae, Hwang | - |
| dc.date.accessioned | 2023-04-10T03:02:34Z | - |
| dc.date.available | 2023-04-10T03:02:34Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.uri | https://link.springer.com/article/10.1007/s10994-023-06324-x | - |
| dc.identifier.uri | https://dlib.phenikaa-uni.edu.vn/handle/PNK/7709 | - |
| dc.description | CC BY | vi |
| dc.description.abstract | Theoretically, domain adaptation is a well-researched problem. Further, this theory has been well-used in practice. In particular, we note the bound on target error given by Ben-David et al. (Mach Learn 79(1–2):151–175, 2010) and the well-known domain-aligning algorithm based on this work using Domain Adversarial Neural Networks (DANN) presented by Ganin and Lempitsky (in International conference on machine learning, pp 1180–1189). Recently, multiple variants of DANN have been proposed for the related problem of domain generalization, but without much discussion of the original motivating bound. In this paper, we investigate the validity of DANN in domain generalization from this perspective. We investigate conditions under which application of DANN makes sense and further consider DANN as a dynamic process during training. | vi |
| dc.language.iso | en | vi |
| dc.publisher | Springer | vi |
| dc.subject | DANN) | vi |
| dc.subject | dynamic process during training | vi |
| dc.title | Domain adversarial neural networks for domain generalization: when it works and how to improve | vi |
| dc.type | Book | vi |
| Appears in Collections | ||
| OER - Công nghệ thông tin | ||
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