Diffusion-Driven Progressive Target Manipulation for Source-Free Domain Adaptation

• URL: http://arxiv.org/abs/2510.25279v1
• Date: Wed, 29 Oct 2025 08:38:03 GMT
• Title: Diffusion-Driven Progressive Target Manipulation for Source-Free Domain Adaptation
• Authors: Yuyang Huang, Yabo Chen, Junyu Zhou, Wenrui Dai, Xiaopeng Zhang, Junni Zou, Hongkai Xiong, Qi Tian,
• Abstract summary: Source-free domain adaptation (SFDA) is a challenging task that tackles domain shifts using only a pre-trained source model and unlabeled target data.<n>Non-generation SFDA methods suffer from unreliable pseudo-labels in challenging scenarios with large domain discrepancies.<n>We propose a novel generation-based framework named Diffusion-Driven Progressive Target Manipulation.
• Score: 108.0345347464393
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Source-free domain adaptation (SFDA) is a challenging task that tackles domain shifts using only a pre-trained source model and unlabeled target data. Existing SFDA methods are restricted by the fundamental limitation of source-target domain discrepancy. Non-generation SFDA methods suffer from unreliable pseudo-labels in challenging scenarios with large domain discrepancies, while generation-based SFDA methods are evidently degraded due to enlarged domain discrepancies in creating pseudo-source data. To address this limitation, we propose a novel generation-based framework named Diffusion-Driven Progressive Target Manipulation (DPTM) that leverages unlabeled target data as references to reliably generate and progressively refine a pseudo-target domain for SFDA. Specifically, we divide the target samples into a trust set and a non-trust set based on the reliability of pseudo-labels to sufficiently and reliably exploit their information. For samples from the non-trust set, we develop a manipulation strategy to semantically transform them into the newly assigned categories, while simultaneously maintaining them in the target distribution via a latent diffusion model. Furthermore, we design a progressive refinement mechanism that progressively reduces the domain discrepancy between the pseudo-target domain and the real target domain via iterative refinement. Experimental results demonstrate that DPTM outperforms existing methods by a large margin and achieves state-of-the-art performance on four prevailing SFDA benchmark datasets with different scales. Remarkably, DPTM can significantly enhance the performance by up to 18.6% in scenarios with large source-target gaps.

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