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Domain adaptation (DA) learning algorithms (Hu et al., 2024) primarily explore strategies for mitigating domain bias between source and target data distributions. These approaches leverage similarities and discrepancies between domains to transfer and apply models trained in the source domain, thereby enhancing classification performance on the target domain. Recent research has focused on several key directions.
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