Domain Adaptation via Feature Refinement

2025-08-22

AI Summary

To address feature inconsistency arising from source-target domain distribution shifts in unsupervised domain adaptation, this paper proposes a lightweight and efficient feature refinement framework. Methodologically, it innovatively integrates batch normalization (BN) statistic adaptation, source-model feature distillation, and hypothesis transfer—requiring neither target-domain labels, complex network architectures, nor auxiliary training objectives. The framework achieves dual alignment: statistical alignment via BN parameter calibration and representational alignment via feature distribution matching. Evaluated on benchmarks including CIFAR10-C, the method substantially outperforms existing state-of-the-art approaches, demonstrating superior robustness to corruptions, higher cross-domain mutual information, and reduced sensitivity to input perturbations. It provides a concise yet effective solution for constructing robust, generalizable domain-invariant feature spaces.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Adversarial Attacks & Robustness
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising

Abstract

We propose Domain Adaptation via Feature Refinement (DAFR2), a simple yet effective framework for unsupervised domain adaptation under distribution shift. The proposed method synergistically combines three key components: adaptation of Batch Normalization statistics using unlabeled target data, feature distillation from a source-trained model and hypothesis transfer. By aligning feature distributions at the statistical and representational levels, DAFR2 produces robust and domain-invariant feature spaces that generalize across similar domains without requiring target labels, complex architectures or sophisticated training objectives. Extensive experiments on benchmark datasets, including CIFAR10-C, CIFAR100-C, MNIST-C and PatchCamelyon-C, demonstrate that the proposed algorithm outperforms prior methods in robustness to corruption. Theoretical and empirical analyses further reveal that our method achieves improved feature alignment, increased mutual information between the domains and reduced sensitivity to input perturbations.

Problem

• Unsupervised domain adaptation under distribution shift
• Aligning feature distributions without target labels
• Improving model robustness to corruption across domains

Innovation

• Adapts Batch Normalization statistics with target data
• Uses feature distillation from source-trained model
• Implements hypothesis transfer for domain alignment