arXiv:2512.04524v4 Announce Type: replace-cross Abstract: Domain adaptive retrieval aims to transfer knowledge from a labeled source domain to an unlabeled target domain, enabling effective retrieval while mitigating domain discrepancies. However, existing methods encounter several fundamental limitations: 1) neglecting class-level semantic alignment and excessively pursuing pair-wise sample alignment; 2) lacking either pseudo-label reliability consideration or geometric guidance for assessing label correctness; 3) directly quantizing original features affected by domain shift, undermining the
The continuous evolution of AI and machine learning techniques necessitates ongoing research into improving model performance and generalization across diverse data distributions, addressing current limitations in domain adaptation.
This research contributes to more robust and effective retrieval systems in AI, crucial for applications ranging from search engines to recommendation systems, by improving how models transfer knowledge between different data domains.
Retrieval systems can become more accurate and adaptive, reducing the need for extensive re-labeling or re-training when deployed in new, unlabeled environments.
- · AI/ML researchers
- · Companies with large, disparate datasets
- · Search engine providers
- · Recommendation system developers
- · Developers relying solely on brute-force data labeling
- · Companies with stagnant AI infrastructure
Improved performance of AI systems in real-world scenarios with varied data distributions.
Reduced operational costs for deploying and maintaining AI applications across different domains.
Acceleration of AI adoption in industries where data heterogeneity has been a significant barrier.
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Read at arXiv cs.AI