A Step Towards Robust Unsupervised Domain Adaptation via Fine-Tuning and Reinforcement Learning

• URL: http://arxiv.org/abs/2607.03600v1
• Date: Fri, 03 Jul 2026 20:56:12 GMT
• Title: A Step Towards Robust Unsupervised Domain Adaptation via Fine-Tuning and Reinforcement Learning
• Abstract summary: We propose textbfSFT+RL, a two-stage robust UDA framework that integratesSupervised Fine Tuning and Reinforcement Learning.<n>textbfSFT+RL achieves average improvements of textbf10.2% in clean accuracy and textbf15.8% in adversarial robustness across three datasets.
• Score: 2.436631469537453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adversarial robustness in Unsupervised Domain Adaptation (UDA) remains a significant challenge due to noisy pseudo labels and inherent distributional shifts between the clean source and adversarially perturbed target domains. Existing approaches often fail to achieve an optimal trade-off between robustness and accuracy, as pseudo-labels generated by domain-adapted models tend to introduce classification errors under adversarial attacks. In this work, we propose \textbf{SFT+RL}, a two-stage robust UDA framework that integrates Supervised Fine Tuning (SFT) and Reinforcement Learning (RL) on top of CLIP's pre-trained visual encoder. In the SFT stage, we adversarially fine-tune a linear classifier using PGD-based perturbations over the labelled source domain while partially unfreezing CLIP's projection layer. It allows adaptation to adversarial noise while preserving CLIP's rich semantic priors. We introduce a confidence-guided pseudo-labeling strategy in the RL stage to annotate unlabeled target samples progressively. Pseudo labels are filtered using a decaying confidence threshold to balance quality and coverage, and the model is trained on a composite dataset formed by combining clean source samples with high-confidence target samples. Adversarial training is applied to mixed batches of clean and adversarial examples to enhance cross-domain robustness. Comprehensive evaluations on three benchmark datasets OfficeHome~\cite{tomm-ude}, PACS~\cite{pacs}, and VisDA~\cite{visda} demonstrate the effectiveness of our approach. Notably, \textbf{SFT+RL} achieves average improvements of \textbf{10.2\%} in clean accuracy and \textbf{15.8\%} in adversarial robustness across all three datasets, outperforming existing state-of-the-art methods.

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