Domain Adaptation via Feature Refinement

TL;DR

This paper introduces DAFR2, a straightforward framework for unsupervised domain adaptation that aligns feature distributions through statistical and representational methods, improving robustness without complex training.

Contribution

DAFR2 combines batch normalization adaptation, feature distillation, and hypothesis transfer to enhance domain-invariant features without requiring target labels or complex architectures.

Findings

Outperforms prior methods on benchmark datasets.

Improves robustness to corruption and input perturbations.

Achieves better feature alignment and mutual information.

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.