When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts
arXiv:2507.14661v2 Announce Type: replace-cross Abstract: Semi-supervised domain adaptation (SSDA) seeks to achieve accurate predictions in a target domain with limited labeled target data by exploiting abundant source and unlabeled target data. We study this problem under structural causal models (SCMs), which provide a statistical framework to describe distribution shifts between source and target domains as interventions in the data-generating process rather than ad hoc changes in model parameters. The central phenomenon is that, under low-dimensional interventions, source and unlabeled tar
The paper tackles a core challenge in AI development—efficient adaptation to new data with limited labels—which is becoming increasingly critical as models scale and domain shifts are common.
This research provides a theoretical framework for semi-supervised domain adaptation using structural causal models, offering a more robust and efficient method for AI model deployment in real-world scenarios.
The ability to fine-tune AI models with fewer labeled target data points changes the economics and feasibility of deploying AI in new, data-scarce environments, potentially accelerating AI adoption across diverse sectors.
- · AI developers
- · Industries with limited labeled data
- · AI-driven product companies
- · Companies relying on extensive manual data labeling
- · Less efficient domain adaptation methods
More cost-effective and faster deployment of AI solutions in varied domains.
Increased accessibility of advanced AI to smaller organizations and niche markets due to reduced data requirements.
Acceleration of industry transformation as AI can be adapted more readily to unique operational contexts and data sets.
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Read at arXiv cs.LG