ProCal: Probability Calibration for Neighborhood-Guided Source-Free Domain Adaptation

2026-03-19

AI Summary

This work addresses the challenges of source knowledge forgetting and overfitting to local noise in unsupervised domain adaptation, which often arise from an overreliance on neighborhood prediction similarity. To mitigate these issues, the authors propose a dynamic probability calibration method that leverages a dual-model co-prediction mechanism to adaptively calibrate neighborhood probabilities by fusing the initial outputs of the source model with the online predictions of the current model. The approach jointly optimizes a soft supervision loss and a diversity loss, effectively preserving discriminative source-domain information while suppressing interference from local noise. Extensive experiments across 31 cross-domain tasks on four public datasets demonstrate that the proposed method significantly alleviates knowledge forgetting and overfitting, achieving a balanced integration of source knowledge and target-specific information.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Reasoning under Uncertainty: Relational Probabilistic Models
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Source-Free Domain Adaptation (SFDA) adapts pre-trained models to unlabeled target domains without requiring access to source data. Although state-of-the-art methods leveraging local neighborhood structures show promise for SFDA, they tend to over-rely on prediction similarity among neighbors. This over-reliance accelerates the forgetting of source knowledge and increases susceptibility to local noise overfitting. To address these issues, we introduce ProCal, a probability calibration method that dynamically calibrates neighborhood-based predictions through a dual-model collaborative prediction mechanism. ProCal integrates the source model's initial predictions with the current model's online outputs to effectively calibrate neighbor probabilities. This strategy not only mitigates the interference of local noise but also preserves the discriminative information from the source model, thereby achieving a balance between knowledge retention and domain adaptation. Furthermore, we design a joint optimization objective that combines a soft supervision loss with a diversity loss to guide the target model. Our theoretical analysis shows that ProCal converges to an equilibrium where source knowledge and target information are effectively fused, reducing both knowledge forgetting and overfitting. We validate the effectiveness of our approach through extensive experiments on 31 cross-domain tasks across four public datasets. Our code is available at: https://github.com/zhengyinghit/ProCal.

Problem

• Source-Free Domain Adaptation
• Probability Calibration
• Neighborhood Structure
• Knowledge Forgetting
• Noise Overfitting

Innovation

• Probability Calibration
• Source-Free Domain Adaptation
• Neighborhood Guidance
• Dual-Model Collaboration
• Knowledge Retention