Trust-Aware Joint Feature-Prediction Discrepancy for Robust Domain Adaptation

2026-05-24

AI Summary

This work addresses performance degradation in domain adaptation caused by distributional shift by proposing a trust-aware domain adaptation framework. It introduces, for the first time, a sample-level trust mechanism into the joint alignment of feature and prediction spaces. The approach constructs dual trust indicators based on predictive entropy—quantifying uncertainty—and prototype similarity—measuring semantic consistency—to weight the Joint Feature-Prediction Discrepancy (JFPD) metric. This weighting strategy steers the model to focus on target samples that are both reliable and semantically consistent. Extensive experiments demonstrate that the proposed method significantly outperforms state-of-the-art approaches on standard benchmarks. Moreover, the estimated domain discrepancy exhibits a strong correlation with target-domain error, confirming the method’s effectiveness and robustness.

Abstract

Domain adaptation aims to mitigate performance degradation caused by distribution shifts between a labeled source domain and an unlabeled or sparsely labeled target domain. Most existing approaches estimate domain discrepancy either in feature space or in prediction space. However, these single-perspective strategies overlook a critical problem under domain shift: the reliability of the signals used for alignment. In practice, both learned representations and semantic predictions may become unreliable, and treating all target samples equally can lead to misleading alignment and suboptimal transfer. We introduce trust-aware domain adaptation, a principled framework that models domain discrepancy through the reliability of feature and prediction signals. Central to our approach is the Joint Feature-Prediction Discrepancy (JFPD), a unified formulation that jointly captures representation divergence and prediction divergence while weighting their contributions by sample-specific trust. Trust is quantified via two complementary mechanisms: uncertainty-aware trust, derived from prediction entropy to suppress unreliable predictions, and semantic-alignment trust, computed from prototype similarity in feature space to emphasize well-aligned representations. By prioritizing confident and semantically consistent samples while down-weighting noisy or ambiguous ones, JFPD provides a reliability-aware estimate of domain discrepancy. We further integrate JFPD into a training objective that guides adaptation toward trustworthy regions of the target domain. Experiments on standard benchmarks demonstrate that the proposed framework consistently achieves superior adaptation performance and yields discrepancy estimates that correlate with target-domain error. This work addresses, for the first time, the importance of modeling trust in the interaction between features and predictions for domain adaptation.

Problem

• domain adaptation
• distribution shift
• trustworthiness
• feature-prediction discrepancy
• reliable alignment

Innovation

• trust-aware domain adaptation
• joint feature-prediction discrepancy
• uncertainty-aware trust
• semantic-alignment trust
• domain discrepancy