Dynamic Domain Discrepancy Adjustment for Active Multi-Domain Adaptation
- URL: http://arxiv.org/abs/2307.14068v1
- Date: Wed, 26 Jul 2023 09:40:19 GMT
- Title: Dynamic Domain Discrepancy Adjustment for Active Multi-Domain Adaptation
- Authors: Long Liu, Bo Zhou, Zhipeng Zhao, Zening Liu
- Abstract summary: Multi-source unsupervised domain adaptation (MUDA) aims to transfer knowledge from related source domains to an unlabeled target domain.
We propose a novel approach called Dynamic Domain Discrepancy Adjustment for Active Multi-Domain Adaptation (D3AAMDA)
This mechanism controls the alignment level of features between each source domain and the target domain, effectively leveraging the local advantageous feature information within the source domains.
- Score: 3.367755441623275
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Multi-source unsupervised domain adaptation (MUDA) aims to transfer knowledge
from related source domains to an unlabeled target domain. While recent MUDA
methods have shown promising results, most focus on aligning the overall
feature distributions across source domains, which can lead to negative effects
due to redundant features within each domain. Moreover, there is a significant
performance gap between MUDA and supervised methods. To address these
challenges, we propose a novel approach called Dynamic Domain Discrepancy
Adjustment for Active Multi-Domain Adaptation (D3AAMDA). Firstly, we establish
a multi-source dynamic modulation mechanism during the training process based
on the degree of distribution differences between source and target domains.
This mechanism controls the alignment level of features between each source
domain and the target domain, effectively leveraging the local advantageous
feature information within the source domains. Additionally, we propose a
Multi-source Active Boundary Sample Selection (MABS) strategy, which utilizes a
guided dynamic boundary loss to design an efficient query function for
selecting important samples. This strategy achieves improved generalization to
the target domain with minimal sampling costs. We extensively evaluate our
proposed method on commonly used domain adaptation datasets, comparing it
against existing UDA and ADA methods. The experimental results unequivocally
demonstrate the superiority of our approach.
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